diff --git a/delegate/classic/src/Redefine.hpp b/delegate/classic/src/Redefine.hpp
index 6b10e44..c3422a2 100644
--- a/delegate/classic/src/Redefine.hpp
+++ b/delegate/classic/src/Redefine.hpp
@@ -166,6 +166,18 @@
         return kTfLiteError;
     }
 
+    // Check the target shape to check if there is zero in the shape.
+    if (std::find(targetShape.begin(), targetShape.end(), 0) != targetShape.end() &&
+        inputTensorInfo0.GetNumElements() != 0)
+    {
+        TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext,
+                                 "TfLiteArmnnDelegate: Input to reshape is a tensor with elements, "
+                                 "but the requested shape has 0. "
+                                 "operator #%d node #%d: ",
+                                 operatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
     // Use the data to create the required tensor shape.
     if (CreateOutputTensorShape(inputTensorInfo0, targetShape, reshapeDesc) != kTfLiteOk)
     {
diff --git a/delegate/common/src/DelegateUtils.hpp b/delegate/common/src/DelegateUtils.hpp
index a74ed8b..a2cdc83 100644
--- a/delegate/common/src/DelegateUtils.hpp
+++ b/delegate/common/src/DelegateUtils.hpp
@@ -186,7 +186,16 @@
                         std::accumulate(targetShape.begin(), targetShape.end(), -1, std::multiplies<int32_t>()));
 
         auto stretchIndex = static_cast<size_t>(std::distance(targetShape.begin(), stretchDim));
-        outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements;
+
+        if (targetNumElements == 0)
+        {
+            // To handle the edge case that input and output both have zero elements
+            outputDims[stretchIndex] = 0;
+        }
+        else
+        {
+            outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements;
+        }
     }
 
     armnn::TensorShape outputShape = armnn::TensorShape(static_cast<unsigned int>(outputDims.size()),
diff --git a/delegate/opaque/src/Redefine.hpp b/delegate/opaque/src/Redefine.hpp
index 5ce7a3d..6319ca7 100644
--- a/delegate/opaque/src/Redefine.hpp
+++ b/delegate/opaque/src/Redefine.hpp
@@ -201,6 +201,19 @@
         return kTfLiteError;
     }
 
+    // Check the target shape to check if there is zero in the shape.
+    if (std::find(targetShape.begin(), targetShape.end(), 0) != targetShape.end() &&
+        inputTensorInfo0.GetNumElements() != 0)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Input to reshape is a tensor with elements, "
+                "but the requested shape has 0. "
+                "operator #%d node #%d: ",
+                operatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
     // Use the data to create the required tensor shape.
     if (CreateOutputTensorShape(inputTensorInfo0, targetShape, reshapeDesc) != kTfLiteOk)
     {
diff --git a/src/armnnDeserializer/Deserializer.cpp b/src/armnnDeserializer/Deserializer.cpp
index 6e2c07b..f455b1a 100644
--- a/src/armnnDeserializer/Deserializer.cpp
+++ b/src/armnnDeserializer/Deserializer.cpp
@@ -2632,7 +2632,23 @@
                std::accumulate(targetDimsIn.begin(), targetDimsIn.end(), -1, std::multiplies<int32_t>()));
 
         auto stretchIndex = static_cast<size_t>(std::distance(targetDimsIn.begin(), stretchDim));
-        outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements;
+        if (targetNumElements == 0)
+        {
+            if (inputTensorInfo.GetNumElements() == 0)
+            {
+                outputDims[stretchIndex] = 0;
+            }
+            else
+            {
+                throw ParseException(
+                        fmt::format("Input to reshape is a tensor with elements, but the requested shape has 0. {}",
+                                    CHECK_LOCATION().AsString()));
+            }
+        }
+        else
+        {
+            outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements;
+        }
     }
 
     TensorShape outputShape = TensorShape(static_cast<unsigned int>(outputDims.size()), outputDims.data());
diff --git a/src/armnnOnnxParser/OnnxParser.cpp b/src/armnnOnnxParser/OnnxParser.cpp
index f165df9..5ec99ed 100644
--- a/src/armnnOnnxParser/OnnxParser.cpp
+++ b/src/armnnOnnxParser/OnnxParser.cpp
@@ -441,10 +441,26 @@
                             CHECK_LOCATION().AsString()));
         }
 
-        auto targetNumElements = armnn::numeric_cast<unsigned int>(std::accumulate(targetDims.begin(), targetDims.end(),
-            -1, std::multiplies<int32_t>()));
+        auto targetNumElements = armnn::numeric_cast<unsigned int>(
+                std::accumulate(targetDims.begin(), targetDims.end(), -1, std::multiplies<int32_t>()));
         auto stretchIndex = static_cast<size_t>(std::distance(targetDims.begin(), stretchDim));
-        outDims[stretchIndex] = inShape.GetNumElements() / targetNumElements;
+        if (targetNumElements == 0)
+        {
+            if (inShape.GetNumElements() == 0)
+            {
+                outDims[stretchIndex] = 0;
+            }
+            else
+            {
+                throw ParseException(
+                        fmt::format("Input to reshape is a tensor with elements, but the requested shape has 0. {}",
+                                    CHECK_LOCATION().AsString()));
+            }
+        }
+        else
+        {
+            outDims[stretchIndex] = inShape.GetNumElements() / targetNumElements;
+        }
     }
     TensorShape outShape = TensorShape{static_cast<unsigned int>(outDims.size()), outDims.data()};
     return TensorInfo(outShape, dataType);
diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp
index c2be54f..30875d5 100644
--- a/src/armnnTfLiteParser/TfLiteParser.cpp
+++ b/src/armnnTfLiteParser/TfLiteParser.cpp
@@ -3252,6 +3252,7 @@
     auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex));
     RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});
 }
+
 armnn::TensorInfo TfLiteParserImpl::OutputShapeOfReshape(const armnn::TensorInfo& inputTensorInfo,
                                                          const std::vector<int32_t>& targetDimsIn)
 {
@@ -3271,7 +3272,24 @@
                 std::accumulate(targetDimsIn.begin(), targetDimsIn.end(), -1, std::multiplies<int32_t>()));
 
         auto stretchIndex = static_cast<size_t>(std::distance(targetDimsIn.begin(), stretchDim));
-        outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements;
+
+        if (targetNumElements == 0)
+        {
+            if (inputTensorInfo.GetNumElements() == 0)
+            {
+                outputDims[stretchIndex] = 0;
+            }
+            else
+            {
+                throw ParseException(
+                        fmt::format("Input to reshape is a tensor with elements, but the requested shape has 0. {}",
+                                    CHECK_LOCATION().AsString()));
+            }
+        }
+        else
+        {
+            outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements;
+        }
     }
 
     TensorShape outputShape = TensorShape(static_cast<unsigned int>(outputDims.size()), outputDims.data());