diff --git a/onnxruntime/core/providers/webgpu/tensor/scatter_nd.cc b/onnxruntime/core/providers/webgpu/tensor/scatter_nd.cc
index f13e86c185928..9f07e2d2a3988 100644
--- a/onnxruntime/core/providers/webgpu/tensor/scatter_nd.cc
+++ b/onnxruntime/core/providers/webgpu/tensor/scatter_nd.cc
@@ -146,24 +146,24 @@ Status ScatterND::ComputeInternal(ComputeContext& context) const {
   const auto* updates = context.Input<Tensor>(2);
   const auto& input_shape = input->Shape();
   const auto& indices_shape = indices->Shape();
-  auto indices_rank = indices_shape.NumDimensions();
-  auto last_index_dimension = static_cast<uint32_t>(indices_shape[indices_rank - 1]);
-  auto num_updates_elements = static_cast<uint32_t>(input_shape.SizeFromDimension(last_index_dimension));
-  // TODO: support bool with components 4.
-  const size_t components = 1;
-  auto output_size = static_cast<uint32_t>((indices_shape.SizeToDimension(indices_rank - 1) + components - 1) / components);
   auto* output = context.Output(0, input_shape);
-  if (output_size == 0) {
-    // If the output tensor is empty, we can return early.
-    return Status::OK();
-  }
-  MLDataType data_type = input->DataType();
   const void* source = input->DataRaw();
   void* target = output->MutableDataRaw();
   // If source and target pointers are not equal (non-inplace operation), we need to copy the data.
   if (target != source) {
     ORT_RETURN_IF_ERROR(Info().GetDataTransferManager().CopyTensor(*input, *output));
   }
+  if (indices_shape.Size() == 0) {
+    // If the indices are empty, we can return early.
+    return Status::OK();
+  }
+  auto indices_rank = indices_shape.NumDimensions();
+  auto last_index_dimension = static_cast<uint32_t>(indices_shape[indices_rank - 1]);
+  auto num_updates_elements = static_cast<uint32_t>(input_shape.SizeFromDimension(last_index_dimension));
+  // TODO: support bool with components 4.
+  const size_t components = 1;
+  auto output_size = static_cast<uint32_t>((indices_shape.SizeToDimension(indices_rank - 1) + components - 1) / components);
+  MLDataType data_type = input->DataType();
   ScatterNDProgram program(reduction_, data_type);
   program
       .CacheHint(static_cast<uint32_t>(reduction_))
diff --git a/onnxruntime/test/providers/cpu/tensor/scatter_nd_op_test.cc b/onnxruntime/test/providers/cpu/tensor/scatter_nd_op_test.cc
index 895c8ab3e53e4..e6d113e1e4dca 100644
--- a/onnxruntime/test/providers/cpu/tensor/scatter_nd_op_test.cc
+++ b/onnxruntime/test/providers/cpu/tensor/scatter_nd_op_test.cc
@@ -235,5 +235,16 @@ TEST(ScatterNDOpTest, ScatterND_18_max) {
   test1.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider});
 }
 
+// Test for ScatterND with empty indices - output should be same as input
+TEST(ScatterNDOpTest, ScatterND_empty_indices) {
+  // Test with float data type and minimal empty case
+  OpTester test1("ScatterND", 11);
+  test1.AddInput<float>("data", {2, 3}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f});
+  test1.AddInput<int64_t>("indices", {0, 1}, {});                                  // Empty indices tensor - no indices to process
+  test1.AddInput<float>("updates", {0, 3}, {});                                    // Empty updates tensor
+  test1.AddOutput<float>("output", {2, 3}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f});  // Same as input
+  test1.Run(OpTester::ExpectResult::kExpectSuccess, "", {kDmlExecutionProvider});
+}
+
 }  // namespace test
 }  // namespace onnxruntime