diff --git a/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc b/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc
index 655364357999a..3727ac0918115 100644
--- a/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc
+++ b/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc
@@ -254,6 +254,21 @@ Status SkipLayerNormFusion::ApplyImpl(Graph& graph, bool& modified, int graph_le
       continue;
     }
 
+    // SkipLayerNormalization kernel requires gamma and beta to be 1D.
+    // Skip fusion if gamma or beta have more than 1 dimension.
+    const NodeArg* gamma_arg = ln_node.MutableInputDefs()[1];
+    const TensorShapeProto* gamma_shape = gamma_arg->Shape();
+    if (gamma_shape != nullptr && gamma_shape->dim_size() != 1) {
+      continue;
+    }
+    if (ln_node.MutableInputDefs().size() > 2) {
+      const NodeArg* beta_arg = ln_node.MutableInputDefs()[2];
+      const TensorShapeProto* beta_shape = beta_arg->Shape();
+      if (beta_shape != nullptr && beta_shape->dim_size() != 1) {
+        continue;
+      }
+    }
+
     NodeArg beta_place_holder("", nullptr);
 
     // Get the inputs for the new SkipLayerNormalization node.
diff --git a/onnxruntime/test/optimizer/graph_transform_test_layernorm.cc b/onnxruntime/test/optimizer/graph_transform_test_layernorm.cc
index 0afb836192b0a..4615b6a57b558 100644
--- a/onnxruntime/test/optimizer/graph_transform_test_layernorm.cc
+++ b/onnxruntime/test/optimizer/graph_transform_test_layernorm.cc
@@ -638,6 +638,37 @@ TEST_F(GraphTransformationTests, SkipLayerNormFusionTest) {
   TestSkipLayerNormFusion(MODEL_FOLDER "fusion/skip_layer_norm_format3_graph_output.onnx", 1, 1, 0, 0, logger_.get());
 }
 
+// SkipLayerNorm fusion should not be applied when gamma/beta have more than 1 dimension,
+// because the SkipLayerNormalization kernel requires 1D gamma/beta.
+TEST_F(GraphTransformationTests, SkipLayerNormFusion_3DGamma_NoFusion) {
+  auto build_test_case = [](ModelTestBuilder& builder) {
+    // Inputs: A and B are 3D [16, 32, 4]
+    auto* input_a = builder.MakeInput<float>({16, 32, 4}, -1.0f, 1.0f);
+    auto* input_b = builder.MakeInput<float>({16, 32, 4}, -1.0f, 1.0f);
+    // gamma and beta have 3D shape [1, 1, 4] (not 1D)
+    auto* gamma = builder.MakeInitializer<float>({1, 1, 4}, {1.0f, 2.0f, 3.0f, 4.0f});
+    auto* beta = builder.MakeInitializer<float>({1, 1, 4}, {0.1f, 0.2f, 0.3f, 0.4f});
+    auto* add_out = builder.MakeIntermediate();
+    auto* ln_out = builder.MakeOutput();
+
+    builder.AddNode("Add", {input_a, input_b}, {add_out});
+    builder.AddNode("LayerNormalization", {add_out, gamma, beta}, {ln_out})
+        .AddAttribute("axis", static_cast<int64_t>(-1));
+  };
+
+  auto post_graph_checker = [](Graph& graph) {
+    // SkipLayerNormalization should NOT have been created because gamma/beta are 3D.
+    TEST_RETURN_IF_NOT(CountOpsInGraph(graph)["Add"] == 1);
+    TEST_RETURN_IF_NOT(CountOpsInGraph(graph)["LayerNormalization"] == 1);
+    TEST_RETURN_IF_NOT(CountOpsInGraph(graph)["com.microsoft.SkipLayerNormalization"] == 0);
+    return Status::OK();
+  };
+
+  ASSERT_STATUS_OK(TestGraphTransformer(build_test_case, 17, *logger_,
+                                        std::make_unique<SkipLayerNormFusion>(),
+                                        TransformerLevel::Level2, 1, nullptr, post_graph_checker));
+}
+
 TEST_F(GraphTransformationTests, GroupQueryAttentionFusionTest) {
   TestGQAFusion(MODEL_FOLDER "fusion/gqa_fusion_quantized_simple.onnx", 1, 0, logger_.get());
   TestGQAFusion(MODEL_FOLDER "fusion/gqa_fusion_different_head_sizes.onnx", 0, 1, logger_.get());