diff --git a/onnxruntime/core/providers/webnn/builders/impl/softmax_op_builder.cc b/onnxruntime/core/providers/webnn/builders/impl/softmax_op_builder.cc
index b207b804416aa..6a86ca7aca6e9 100644
--- a/onnxruntime/core/providers/webnn/builders/impl/softmax_op_builder.cc
+++ b/onnxruntime/core/providers/webnn/builders/impl/softmax_op_builder.cc
@@ -35,30 +35,79 @@ Status SoftmaxOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
   std::vector<int64_t> input_shape;
   ORT_RETURN_IF_NOT(GetShape(*input_defs[0], input_shape, logger), "Cannot get shape");
   const auto input_size = input_shape.size();
-  // WebNN Softmax only support 2d input shape, reshape input to 2d.
-  if (input_size != 2) {
-    NodeAttrHelper helper(node);
+  NodeAttrHelper helper(node);
+  if (node.SinceVersion() < 13) {
     int32_t axis = helper.Get("axis", 1);
-    if (node.SinceVersion() >= 13)
-      // Opset 13 has default value -1.
-      axis = helper.Get("axis", -1);
+    axis = static_cast<int32_t>(HandleNegativeAxis(axis, input_size));
     //  Coerce the input into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}].
+    if (input_size != 2) {
+      int32_t first_dim = static_cast<int32_t>(std::reduce(input_shape.begin(), input_shape.begin() + axis,
+                                                           1, std::multiplies<int64_t>()));
+      int32_t second_dim = static_cast<int32_t>(std::reduce(input_shape.begin() + axis, input_shape.end(),
+                                                            1, std::multiplies<int64_t>()));
+      emscripten::val new_shape = emscripten::val::array(std::vector<int32_t>{first_dim, second_dim});
+      input = model_builder.GetBuilder().call<emscripten::val>("reshape", input, new_shape);
+    }
+
+    output = model_builder.GetBuilder().call<emscripten::val>("softmax", input);
+
+    // Reshape output to the same shape of input.
+    if (input_size != 2) {
+      emscripten::val new_shape = emscripten::val::array();
+      for (size_t i = 0; i < input_size; i++) {
+        new_shape.call<void>("push", static_cast<int32_t>(input_shape[i]));
+      }
+      output = model_builder.GetBuilder().call<emscripten::val>("reshape", output, new_shape);
+    }
+  } else {
+    int32_t axis = helper.Get("axis", -1);
     axis = static_cast<int32_t>(HandleNegativeAxis(axis, input_size));
-    int32_t first_dim = static_cast<int32_t>(std::reduce(input_shape.begin(), input_shape.begin() + axis,
-                                                         1, std::multiplies<int64_t>()));
-    int32_t second_dim = static_cast<int32_t>(std::reduce(input_shape.begin() + axis, input_shape.end(),
-                                                          1, std::multiplies<int64_t>()));
-    emscripten::val new_shape = emscripten::val::array(std::vector<int32_t>{first_dim, second_dim});
-    input = model_builder.GetBuilder().call<emscripten::val>("reshape", input, new_shape);
-  }
-  output = model_builder.GetBuilder().call<emscripten::val>("softmax", input);
-  // Reshape output to the same shape of input.
-  if (input_size != 2) {
-    emscripten::val new_shape = emscripten::val::array();
-    for (size_t i = 0; i < input_size; i++) {
-      new_shape.call<void>("push", static_cast<int32_t>(input_shape[i]));
+    // Wraparound for transpose the target axis to the last.
+    // WebNN compute the softmax values of the 2-D input tensor along axis 1.
+    // https://www.w3.org/TR/webnn/#api-mlgraphbuilder-softmax-method
+    if (axis != static_cast<int>(input_shape.size() - 1)) {
+      emscripten::val options = emscripten::val::object();
+      std::vector<uint32_t> permutation(input_shape.size());
+      std::iota(permutation.begin(), permutation.end(), 0);
+      permutation.erase(permutation.begin() + axis);
+      permutation.push_back(axis);
+      options.set("permutation", emscripten::val::array(permutation));
+      input = model_builder.GetBuilder().call<emscripten::val>("transpose", input, options);
+    }
+    // Wraparound for reshape input tensor to 2-D.
+    if (input_shape.size() != 2) {
+      uint32_t first_dim = static_cast<uint32_t>(std::reduce(input_shape.begin(), input_shape.begin() + axis,
+                                                             1, std::multiplies<int64_t>()));
+      first_dim *= static_cast<uint32_t>(std::reduce(input_shape.begin() + axis + 1, input_shape.end(),
+                                                     1, std::multiplies<int64_t>()));
+      uint32_t second_dim = static_cast<uint32_t>(input_shape[axis]);
+      emscripten::val new_shape = emscripten::val::array(std::vector<uint32_t>{first_dim, second_dim});
+      input = model_builder.GetBuilder().call<emscripten::val>("reshape", input, new_shape);
+    }
+
+    output = model_builder.GetBuilder().call<emscripten::val>("softmax", input);
+
+    // Transpose back to the axis.
+    if (input_shape.size() != 2) {
+      std::vector<uint32_t> new_shape;
+      std::transform(input_shape.begin(), input_shape.begin() + axis, std::back_inserter(new_shape),
+                     [](int64_t dim) -> uint32_t { return static_cast<uint32_t>(dim); });
+      std::transform(input_shape.begin() + axis + 1, input_shape.end(), std::back_inserter(new_shape),
+                     [](int64_t dim) -> uint32_t { return static_cast<uint32_t>(dim); });
+      new_shape.push_back(static_cast<int32_t>(input_shape[axis]));
+      output = model_builder.GetBuilder().call<emscripten::val>("reshape",
+                                                                output, emscripten::val::array(new_shape));
+    }
+    // Reshape to the original shape.
+    if (axis != static_cast<int>(input_shape.size() - 1)) {
+      emscripten::val options = emscripten::val::object();
+      std::vector<uint32_t> permutation(input_shape.size());
+      std::iota(permutation.begin(), permutation.end(), 0);
+      permutation.pop_back();
+      permutation.insert(permutation.begin() + axis, input_shape.size() - 1);
+      options.set("permutation", emscripten::val::array(permutation));
+      output = model_builder.GetBuilder().call<emscripten::val>("transpose", output, options);
     }
-    output = model_builder.GetBuilder().call<emscripten::val>("reshape", output, new_shape);
   }
   model_builder.AddOperand(node.OutputDefs()[0]->Name(), std::move(output));
   return Status::OK();
@@ -80,14 +129,6 @@ bool SoftmaxOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& /* initiali
                           << input_size << "d shape";
     return false;
   }
-  NodeAttrHelper helper(node);
-  const int64_t axis = helper.Get("axis", 1);
-  // WebNN softmax only support reshape for the last axis or version before 13.
-  // TODO: support opset 13 by composing into: Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1).
-  if (axis != -1 && axis != input_shape.size() - 1 && node.SinceVersion() >= 13) {
-    LOGS(logger, VERBOSE) << "SoftMax only support axis 1 or -1, input axis: " << axis;
-    return false;
-  }
 
   return true;
 }