diff --git a/js/web/lib/wasm/jsep/webgpu/ops/softmax.ts b/js/web/lib/wasm/jsep/webgpu/ops/softmax.ts
index c4e5a94f225da..fbab44e211946 100644
--- a/js/web/lib/wasm/jsep/webgpu/ops/softmax.ts
+++ b/js/web/lib/wasm/jsep/webgpu/ops/softmax.ts
@@ -9,7 +9,8 @@ import { DataType } from '../../../wasm-common';
 import { TensorView } from '../../tensor-view';
 import { ShapeUtil } from '../../util';
 import { AttributeWithCacheKey, createAttributeWithCacheKey } from '../attribute-with-cache-key';
-import { ComputeContext, ProgramInfo } from '../types';
+import { ComputeContext } from '../types';
+import { createTransposeProgramInfo } from './transpose';
 
 import {
   getMaxComponents,
@@ -30,19 +31,32 @@ export interface SoftmaxAttributes extends AttributeWithCacheKey {
   readonly axis: number;
 }
 
-const createSoftmaxProgramInfo = (input: TensorView, attributes: SoftmaxAttributes): ProgramInfo => {
-  const shape = input.dims;
-  const outputSize = ShapeUtil.size(shape);
+const createSoftmaxProgramInfo = (context: ComputeContext, attributes: SoftmaxAttributes) => {
+  const input = context.inputs[0];
+  const inputShape = input.dims;
+  const outputSize = ShapeUtil.size(inputShape);
   const WG = 64;
-  let axis = attributes.axis;
-  if (axis < 0) {
-    axis = shape.length + axis;
-  }
-  if (axis < shape.length - 1) {
-    throw new Error('softmax only supports last axis for now.');
+  const inputRank = inputShape.length;
+  const axis = ShapeUtil.normalizeAxis(attributes.axis, inputRank);
+  const isTransposeRequired = axis < inputShape.length - 1;
+  let transposedInput: TensorView;
+  let perm: number[] = [];
+
+  if (isTransposeRequired) {
+    perm = Array.from({ length: inputRank }, (_, i) => i);
+    perm[axis] = inputRank - 1;
+    perm[inputRank - 1] = axis;
+
+    transposedInput = context.compute(createTransposeProgramInfo(input, perm), {
+      inputs: [input],
+      outputs: [-1],
+    })[0];
+  } else {
+    transposedInput = input;
   }
 
-  const cols = shape[axis];
+  const transposedInputShape = transposedInput.dims;
+  const cols = transposedInputShape[inputRank - 1];
   const rows = outputSize / cols;
   const components = getMaxComponents(cols);
   const packedCols = cols / components;
@@ -58,12 +72,12 @@ const createSoftmaxProgramInfo = (input: TensorView, attributes: SoftmaxAttribut
 
     return name;
   };
-  const x = inputVariable('x', input.dataType, input.dims, components);
-  const output = outputVariable('result', input.dataType, input.dims, components);
+  const x = inputVariable('x', transposedInput.dataType, transposedInput.dims, components);
+  const output = outputVariable('result', transposedInput.dataType, transposedInput.dims, components);
   const valueType = x.type.value;
   // 6.2.4 in wgsl spec
   const threadMaxDecl =
-    tensorTypeToWsglStorageType(input.dataType) === 'f32'
+    tensorTypeToWsglStorageType(transposedInput.dataType) === 'f32'
       ? `var threadMax = ${valueType}(-3.402823e+38f);`
       : `var threadMax = ${valueType}(-65504.0h);`;
   const getShaderSource = (shaderHelper: ShaderHelper) => `
@@ -139,21 +153,33 @@ const createSoftmaxProgramInfo = (input: TensorView, attributes: SoftmaxAttribut
           setValue(row, col, row_stride, value);
         }
       }`;
-  return {
-    name: 'Softmax',
-    shaderCache: { hint: `${components}`, inputDependencies: ['type'] },
-    getRunData: () => ({
-      outputs: [{ dims: shape, dataType: input.dataType }],
-      dispatchGroup: { x: rows },
-      programUniforms: [{ type: DataType.int32, data: packedCols }],
-    }),
-    getShaderSource,
-  };
+  const result = context.compute(
+    {
+      name: 'Softmax',
+      shaderCache: { hint: `${components}`, inputDependencies: ['type'] },
+      getRunData: () => ({
+        outputs: [{ dims: transposedInputShape, dataType: transposedInput.dataType }],
+        dispatchGroup: { x: rows },
+        programUniforms: [{ type: DataType.int32, data: packedCols }],
+      }),
+      getShaderSource,
+    },
+    {
+      inputs: [transposedInput],
+      outputs: [isTransposeRequired ? -1 : 0],
+    },
+  )[0];
+
+  if (isTransposeRequired) {
+    context.compute(createTransposeProgramInfo(result, perm), {
+      inputs: [result],
+    });
+  }
 };
 
 export const softmax = (context: ComputeContext, attributes: SoftmaxAttributes): void => {
   validateInputs(context.inputs);
-  context.compute(createSoftmaxProgramInfo(context.inputs[0], attributes));
+  createSoftmaxProgramInfo(context, attributes);
 };
 
 export const parseSoftmaxAttributes = (attributes: Record<string, unknown>): SoftmaxAttributes =>
diff --git a/js/web/test/data/ops/softmax.jsonc b/js/web/test/data/ops/softmax.jsonc
index 98573fcd73ba2..7fbe2119a0953 100644
--- a/js/web/test/data/ops/softmax.jsonc
+++ b/js/web/test/data/ops/softmax.jsonc
@@ -20,14 +20,7 @@
             "type": "float32"
           }
         ]
-      }
-    ]
-  },
-  {
-    "name": "Softmax with no attributes",
-    "operator": "Softmax",
-    "attributes": [],
-    "cases": [
+      },
       {
         "name": "T[2, 2, 2]",
         "inputs": [
@@ -49,5 +42,63 @@
         ]
       }
     ]
+  },
+  {
+    "name": "Softmax with attribute axis -1",
+    "operator": "Softmax",
+    "attributes": [{ "name": "axis", "data": -1, "type": "int" }],
+    "cases": [
+      {
+        "name": "T[2,2]",
+        "inputs": [
+          {
+            "data": [1.0, 2.0, 3.0, 4.0],
+            "dims": [2, 2],
+            "type": "float32"
+          }
+        ],
+        "outputs": [
+          {
+            "data": [0.2689414322376251, 0.7310585975646973, 0.2689414322376251, 0.7310585975646973],
+            "dims": [2, 2],
+            "type": "float32"
+          }
+        ]
+      }
+    ]
+  },
+  {
+    "name": "Softmax with attribute axis 1",
+    "operator": "Softmax",
+    "attributes": [{ "name": "axis", "data": 1, "type": "int" }],
+    "cases": [
+      {
+        "name": "T[1, 2, 3, 4]",
+        "inputs": [
+          {
+            "data": [
+              1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0,
+              20.0, 21.0, 22.0, 23.0, 24.0
+            ],
+            "dims": [1, 2, 3, 4],
+            "type": "float32"
+          }
+        ],
+        "outputs": [
+          {
+            "data": [
+              0.000006144174221844878, 0.000006144174221844878, 0.000006144174221844878, 0.000006144174221844878,
+              0.000006144174221844878, 0.000006144174221844878, 0.000006144174221844878, 0.000006144174221844878,
+              0.000006144174221844878, 0.000006144174221844878, 0.000006144174221844878, 0.000006144174221844878,
+              0.9999938011169434, 0.9999938011169434, 0.9999938011169434, 0.9999938011169434, 0.9999938011169434,
+              0.9999938011169434, 0.9999938011169434, 0.9999938011169434, 0.9999938011169434, 0.9999938011169434,
+              0.9999938011169434, 0.9999938011169434
+            ],
+            "dims": [1, 2, 3, 4],
+            "type": "float32"
+          }
+        ]
+      }
+    ]
   }
 ]