diff --git a/delegate/CMakeLists.txt b/delegate/CMakeLists.txt
index ef04913..f0b0e97 100644
--- a/delegate/CMakeLists.txt
+++ b/delegate/CMakeLists.txt
@@ -330,6 +330,8 @@
              test/TransposeConvolution2dTest.cpp
              test/TransposeTest.cpp
              test/TransposeTestHelper.hpp
+             test/UnidirectionalSequenceLstmTest.cpp
+             test/UnidirectionalSequenceLstmTestHelper.hpp
              test/UnpackTest.cpp
              test/UnpackTestHelper.hpp)
 
diff --git a/delegate/opaque/CMakeLists.txt b/delegate/opaque/CMakeLists.txt
index a82b75ae..d7aed37 100644
--- a/delegate/opaque/CMakeLists.txt
+++ b/delegate/opaque/CMakeLists.txt
@@ -40,6 +40,7 @@
         src/Split.hpp
         src/StridedSlice.hpp
         src/Transpose.hpp
+        src/UnidirectionalSequenceLstm.hpp
         src/Unpack.hpp)
 
 add_library(armnnOpaqueDelegateObject OBJECT ${armnnOpaqueDelegateObject_sources})
diff --git a/delegate/opaque/src/UnidirectionalSequenceLstm.hpp b/delegate/opaque/src/UnidirectionalSequenceLstm.hpp
index e169697..790f287 100644
--- a/delegate/opaque/src/UnidirectionalSequenceLstm.hpp
+++ b/delegate/opaque/src/UnidirectionalSequenceLstm.hpp
@@ -2,3 +2,344 @@
 // Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
+
+#pragma once
+
+#include <OpaqueDelegateUtils.hpp>
+
+namespace armnnOpaqueDelegate
+{
+
+TfLiteStatus VisitUnidirectionalSequenceLstmOperator(DelegateData& delegateData,
+                                                     TfLiteOpaqueContext* tfLiteContext,
+                                                     TfLiteOpaqueNode* tfLiteNode,
+                                                     int nodeIndex,
+                                                     int32_t operatorCode)
+{
+    auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode);
+    if (numInputs < 2)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Minimum number of inputs (%d != %d) in node #%d",
+                2, numInputs, nodeIndex);
+        return kTfLiteError;
+    }
+
+    // Gather input indices and use to get input tensor.
+    const int* inputTensors;
+    if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
+                nodeIndex);
+        return kTfLiteError;
+    }
+
+    const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
+    if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    // Gather output indices and use to get output tensors.
+    int numOutputs = 0;
+    const int* outputTensors;
+    if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
+                nodeIndex);
+        return kTfLiteError;
+    }
+
+    const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
+    if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    // Set the params structure for the AddUnidirectionalSequenceLstmLayer call
+    // Please refer to each operand at
+    // https://www.tensorflow.org/mlir/tfl_ops#tflunidirectional_sequence_lstm_tflunidirectionalsequencelstmop
+    armnn::LstmInputParams params;
+
+    if (IsOptionalOperandPresent(tfLiteNode, 1))
+    {
+        params.m_InputToInputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 1);
+    }
+
+    params.m_InputToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 2);
+    params.m_InputToCellWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 3);
+    params.m_InputToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 4);
+
+    // Recurrent weight tensors of size {n_cell, n_output}
+    if (IsOptionalOperandPresent(tfLiteNode, 5))
+    {
+        params.m_RecurrentToInputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 5);
+    }
+
+    params.m_RecurrentToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 6);
+    params.m_RecurrentToCellWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 7);
+    params.m_RecurrentToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 8);
+
+    // Peephole weights tensors of size {n_cell}, representing a diagonal matrix.
+    if (IsOptionalOperandPresent(tfLiteNode, 9))
+    {
+        params.m_CellToInputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 9);
+    }
+
+    if (IsOptionalOperandPresent(tfLiteNode, 10))
+    {
+        params.m_CellToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 10);
+    }
+
+    if (IsOptionalOperandPresent(tfLiteNode, 11))
+    {
+        params.m_CellToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 11);
+    }
+
+    // Gates bias tensors of size {n_cell}
+    if (IsOptionalOperandPresent(tfLiteNode, 12))
+    {
+        params.m_InputGateBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 12);
+    }
+
+    params.m_ForgetGateBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 13);
+    params.m_CellBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 14);
+    params.m_OutputGateBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 15);
+
+    // Projection weight tensor of size {n_output, n_cell}
+    if (IsOptionalOperandPresent(tfLiteNode, 16))
+    {
+        params.m_ProjectionWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 16);
+    }
+    // Projection bias tensor of size {n_output}
+    if (IsOptionalOperandPresent(tfLiteNode, 17))
+    {
+        params.m_ProjectionBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 17);
+    }
+
+    // These state tensors are defined as variable tensors, and will be modified by this op.
+    const TfLiteOpaqueTensor* tfLiteOutputStateIn = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[18]);
+    if (!IsValid(tfLiteContext, tfLiteOutputStateIn, operatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+    const TfLiteOpaqueTensor* cellStateIn = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[19]);
+    if (!IsValid(tfLiteContext, cellStateIn, operatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+    armnn::TensorInfo outputStateInInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputStateIn);
+    armnn::TensorInfo cellStateInInfo = GetTensorInfoForTfLiteOpaqueTensor(cellStateIn);
+
+    // Layer norm coefficient tensors of size {n_cell}, representing a diagonal matrix.
+    if (IsOptionalOperandPresent(tfLiteNode, 20))
+    {
+        params.m_InputLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 20);
+    }
+
+    if (IsOptionalOperandPresent(tfLiteNode, 21))
+    {
+        params.m_ForgetLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 21);
+    }
+
+    if (IsOptionalOperandPresent(tfLiteNode, 22))
+    {
+        params.m_CellLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 22);
+    }
+
+    if (IsOptionalOperandPresent(tfLiteNode, 23))
+    {
+        params.m_OutputLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 23);
+    }
+
+    const auto nodeParams =
+            reinterpret_cast<TfLiteUnidirectionalSequenceLSTMParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));
+
+    // set the layer descriptor
+    armnn::UnidirectionalSequenceLstmDescriptor desc;
+    desc.m_ActivationFunc    = NonNegative(nodeParams->activation, nodeIndex);
+    desc.m_ClippingThresCell = nodeParams->cell_clip;
+    desc.m_ClippingThresProj = nodeParams->proj_clip;
+    desc.m_CifgEnabled       = (params.m_InputToInputWeights == nullptr
+                                || params.m_RecurrentToInputWeights == nullptr
+                                || params.m_InputGateBias == nullptr);
+    desc.m_PeepholeEnabled   = (params.m_CellToForgetWeights != nullptr || params.m_CellToOutputWeights != nullptr);
+    desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr);
+    desc.m_LayerNormEnabled  = (params.m_InputLayerNormWeights != nullptr
+                                || params.m_ForgetLayerNormWeights != nullptr
+                                || params.m_CellLayerNormWeights != nullptr
+                                || params.m_OutputLayerNormWeights != nullptr);
+    desc.m_TimeMajor = nodeParams->time_major;
+
+    // Intermediates tensors aren't accessible through the new Opaque Interface yet, so we have to cast it for now.
+    // This should be changed to use the accessor functions once added.
+    auto* classicTfliteNode = reinterpret_cast<const TfLiteNode*>(tfLiteNode);
+
+    if (classicTfliteNode->intermediates->size > 3 && desc.m_LayerNormEnabled)
+    {
+        auto inputIntermediateTensorInfo =
+                GetTensorInfoForTfLiteOpaqueTensor(
+                        TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, classicTfliteNode->intermediates->data[0]));
+        auto forgetIntermediateTensorInfo =
+                GetTensorInfoForTfLiteOpaqueTensor(
+                        TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, classicTfliteNode->intermediates->data[1]));
+        auto cellIntermediateTensorInfo =
+                GetTensorInfoForTfLiteOpaqueTensor(
+                        TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, classicTfliteNode->intermediates->data[2]));
+        auto outputIntermediateTensorInfo =
+                GetTensorInfoForTfLiteOpaqueTensor(
+                        TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, classicTfliteNode->intermediates->data[3]));
+
+        desc.m_InputIntermediateScale  = inputIntermediateTensorInfo.GetQuantizationScale();
+        desc.m_ForgetIntermediateScale = forgetIntermediateTensorInfo.GetQuantizationScale();
+        desc.m_CellIntermediateScale   = cellIntermediateTensorInfo.GetQuantizationScale();
+        desc.m_OutputIntermediateScale = outputIntermediateTensorInfo.GetQuantizationScale();
+    }
+    else
+    {
+        float defaultIntermediate = std::pow(2, -12);
+        desc.m_InputIntermediateScale = defaultIntermediate;
+        desc.m_ForgetIntermediateScale = defaultIntermediate;
+        desc.m_CellIntermediateScale = defaultIntermediate;
+        desc.m_OutputIntermediateScale = defaultIntermediate;
+    }
+    if (classicTfliteNode->intermediates->size > 4)
+    {
+        auto hiddenTensorInfo =
+                GetTensorInfoForTfLiteOpaqueTensor(
+                        TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, classicTfliteNode->intermediates->data[4]));
+        desc.m_HiddenStateScale = hiddenTensorInfo.GetQuantizationScale();
+        desc.m_HiddenStateZeroPoint = hiddenTensorInfo.GetQuantizationOffset();
+    }
+
+    float defaultIntermediate = std::pow(2, -12);
+    desc.m_InputIntermediateScale = defaultIntermediate;
+    desc.m_ForgetIntermediateScale = defaultIntermediate;
+    desc.m_CellIntermediateScale = defaultIntermediate;
+    desc.m_OutputIntermediateScale = defaultIntermediate;
+
+    const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
+    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);
+
+    unsigned int batchSize  = inputTensorInfo.GetShape()[0];
+    unsigned int outputSize = outputTensorInfo.GetShape()[2];
+    unsigned int numUnits   = cellStateInInfo.GetShape()[1];
+
+    armnn::DataType dataType = inputTensorInfo.GetDataType();
+    float qScale = inputTensorInfo.GetQuantizationScale();
+    float qOffset = inputTensorInfo.GetQuantizationOffset();
+
+    armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 3}, dataType, qScale, qOffset);
+    if (!desc.m_CifgEnabled)
+    {
+        scratchBufferTensorInfo = armnn::TensorInfo({batchSize, numUnits * 4}, dataType, qScale, qOffset);
+    }
+    armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits},
+                                             cellStateInInfo.GetDataType(),
+                                             cellStateInInfo.GetQuantizationScale(),
+                                             cellStateInInfo.GetQuantizationOffset());
+
+    armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset);
+
+    armnn::LstmInputParamsInfo paramsInfo;
+    paramsInfo.m_InputToForgetWeights     = &(params.m_InputToForgetWeights->GetInfo());
+    paramsInfo.m_InputToCellWeights       = &(params.m_InputToCellWeights->GetInfo());
+    paramsInfo.m_InputToOutputWeights     = &(params.m_InputToOutputWeights->GetInfo());
+    paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo());
+    paramsInfo.m_RecurrentToCellWeights   = &(params.m_RecurrentToCellWeights->GetInfo());
+    paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo());
+    paramsInfo.m_ForgetGateBias           = &(params.m_ForgetGateBias->GetInfo());
+    paramsInfo.m_CellBias                 = &(params.m_CellBias->GetInfo());
+    paramsInfo.m_OutputGateBias           = &(params.m_OutputGateBias->GetInfo());
+
+    if (!desc.m_CifgEnabled)
+    {
+        paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo());
+        paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo());
+        if (params.m_CellToInputWeights != nullptr)
+        {
+            paramsInfo.m_CellToInputWeights = &(params.m_CellToInputWeights->GetInfo());
+        }
+        paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo());
+    }
+
+    if (desc.m_ProjectionEnabled)
+    {
+        paramsInfo.m_ProjectionWeights = &(params.m_ProjectionWeights->GetInfo());
+        if (params.m_ProjectionBias != nullptr)
+        {
+            paramsInfo.m_ProjectionBias = &(params.m_ProjectionBias->GetInfo());
+        }
+    }
+
+    if (desc.m_PeepholeEnabled)
+    {
+        paramsInfo.m_CellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo());
+        paramsInfo.m_CellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo());
+    }
+
+    if (desc.m_LayerNormEnabled)
+    {
+        if(!desc.m_CifgEnabled)
+        {
+            paramsInfo.m_InputLayerNormWeights = &(params.m_InputLayerNormWeights->GetInfo());
+        }
+        paramsInfo.m_ForgetLayerNormWeights = &(params.m_ForgetLayerNormWeights->GetInfo());
+        paramsInfo.m_CellLayerNormWeights = &(params.m_CellLayerNormWeights->GetInfo());
+        paramsInfo.m_OutputLayerNormWeights = &(params.m_OutputLayerNormWeights->GetInfo());
+    }
+
+    bool isSupported = false;
+    armnn::BackendId setBackend;
+    auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
+    {
+        FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("UNIDIRECTIONAL_SEQUENCE_LSTM",
+                                          tfLiteContext,
+                                          IsUnidirectionalSequenceLstmSupported,
+                                          delegateData.m_Backends,
+                                          isSupported,
+                                          setBackend,
+                                          inputTensorInfo,
+                                          outputStateInInfo,
+                                          cellStateInInfo,
+                                          outputStateOutTensorInfo,
+                                          cellStateOutTensorInfo,
+                                          outputInfo,
+                                          desc,
+                                          paramsInfo);
+    };
+
+    if (!delegateData.m_Network)
+    {
+        validateFunc(outputTensorInfo, isSupported);
+        return isSupported ? kTfLiteOk : kTfLiteError;
+    }
+
+    armnn::IConnectableLayer* layer = delegateData.m_Network->AddUnidirectionalSequenceLstmLayer(desc, params);
+    layer->SetBackendId(setBackend);
+    ARMNN_ASSERT(layer != nullptr);
+
+    layer->GetOutputSlot(0).SetTensorInfo(outputStateOutTensorInfo);
+    layer->GetOutputSlot(1).SetTensorInfo(cellStateOutTensorInfo);
+    layer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo);
+
+    // Connect the inputs
+    // input_layer
+    delegateData.m_OutputSlotForNode[inputTensors[0]]->Connect(layer->GetInputSlot(0));
+    // cellStateIn
+    delegateData.m_OutputSlotForNode[inputTensors[18]]->Connect(layer->GetInputSlot(1));
+    //outputStateIn
+    delegateData.m_OutputSlotForNode[inputTensors[19]]->Connect(layer->GetInputSlot(2));
+
+    armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(2);
+    delegateData.m_OutputSlotForNode[static_cast<unsigned long>(outputTensors[0])] = &outputSlot;
+
+    return kTfLiteOk;
+}
+
+} // namespace armnnOpaqueDelegate
\ No newline at end of file
diff --git a/delegate/opaque/src/armnn_delegate.cpp b/delegate/opaque/src/armnn_delegate.cpp
index cae1ea5..fa64679 100644
--- a/delegate/opaque/src/armnn_delegate.cpp
+++ b/delegate/opaque/src/armnn_delegate.cpp
@@ -1131,6 +1131,12 @@
                                             tfLiteNode,
                                             nodeIndex,
                                             kTfLiteBuiltinTransposeConv);
+        case kTfLiteBuiltinUnidirectionalSequenceLstm:
+            return VisitUnidirectionalSequenceLstmOperator(delegateData,
+                                                           tfLiteContext,
+                                                           tfLiteNode,
+                                                           nodeIndex,
+                                                           kTfLiteBuiltinUnidirectionalSequenceLstm);
         case kTfLiteBuiltinUnpack:
             return VisitUnpackOperator(delegateData,
                                        tfLiteContext,