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diff --git a/testing/web-platform/tests/webnn/validation_tests/batchNormalization.https.any.js b/testing/web-platform/tests/webnn/validation_tests/batchNormalization.https.any.js
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+// META: title=validation tests for WebNN API batchNormalization operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+let meanIndex = 0;
+let varianceIndex = 0;
+
+const kExampleInputDescriptor = {
+  dataType: 'float32',
+  dimensions: [2, 2]
+};
+// 1D tensor descriptor which may be used for `mean`, `variance`, `scale`, or
+// `bias` inputs.
+const kExample1DTensorDescriptor = {
+  dataType: 'float32',
+  dimensions: [kExampleInputDescriptor.dimensions[/* axis */ 1]]
+};
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    const invalidAxisArray = generateOutOfRangeValuesArray(unsignedLongType);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [size]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [size]});
+      for (let invalidAxis of invalidAxisArray) {
+        assert_throws_js(TypeError, () => builder.batchNormalization(input, mean, variance, {axis: invalidAxis}));
+      }
+    }
+  }
+}, "[batchNormalization] TypeError is expected if options.axis is outside the 'unsigned long' value range");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [size]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [size]});
+      assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis: getRank(dimensions2D)}));
+    }
+  }
+}, "[batchNormalization] DataError is expected if options.axis is 'unsigned long' and it's not in the range 0 to the rank of input, exclusive");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [size]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [size]});
+      for (let axis of notUnsignedLongAxisArray) {
+        assert_false(typeof axis === 'number' && Number.isInteger(axis), "[batchNormalization] options.axis should be of 'unsigned long'");
+        assert_throws_js(TypeError, () => builder.batchNormalization(input, mean, variance, {axis}));
+      }
+    }
+  }
+}, '[batchNormalization] TypeError is expected if options.axis is not an unsigned long interger');
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [dimensions2D[axis]]});
+      for (let dimensions of allWebNNDimensionsArray) {
+        if (dimensions.length !== 1) {
+          // set mean not be 1D tensor
+          const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions});
+          assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance));
+        }
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if the size of mean.dimensions is not 1");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [size]});
+      for (let offset of adjustOffsetsArray) {
+        const adjustedSize = size + offset;
+        const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [adjustedSize]});
+        assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis}));
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if mean.dimensions[0] is not equal to input.dimensions[options.axis]");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [dimensions2D[axis]]});
+      for (let dimensions of allWebNNDimensionsArray) {
+        if (dimensions.length !== 1) {
+          // set variance not be 1D tensor
+          const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions});
+          assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance));
+        }
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if the size of variance.dimensions is not 1");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [size]});
+      for (let offset of adjustOffsetsArray) {
+        const adjustedSize = size + offset;
+        const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [adjustedSize]});
+        assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis}));
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if variance.dimensions[0] is not equal to input.dimensions[options.axis]");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [dimensions2D[axis]]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [dimensions2D[axis]]});
+      for (let dimensions of allWebNNDimensionsArray) {
+        if (dimensions.length !== 1) {
+          // set scale not be 1D tensor
+          const scale = builder.input('scale', {dataType, dimensions});
+          assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis, scale}));
+        }
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if the size of scale.dimensions is not 1");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [size]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [size]});
+      for (let offset of adjustOffsetsArray) {
+        const adjustedSize = size + offset;
+        const scale = builder.input('scale', {dataType, dimensions: [adjustedSize]});
+        assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis, scale}));
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if scale.dimensions[0] is not equal to input.dimensions[options.axis]");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [dimensions2D[axis]]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [dimensions2D[axis]]});
+      for (let dimensions of allWebNNDimensionsArray) {
+        if (dimensions.length !== 1) {
+          // set bias not be 1D tensor
+          const bias = builder.input('bias', {dataType, dimensions});
+          assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis, bias}));
+        }
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if the size of bias.dimensions is not 1");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [size]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [size]});
+      for (let offset of adjustOffsetsArray) {
+        const adjustedSize = size + offset;
+        const bias = builder.input('bias', {dataType, dimensions: [adjustedSize]});
+        assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis, bias}));
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if bias.dimensions[0] is not equal to input.dimensions[options.axis]");
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', kExampleInputDescriptor);
+
+  const mean = builder.input('mean', kExample1DTensorDescriptor);
+  const variance = builder.input('variance', kExample1DTensorDescriptor);
+  assert_throws_js(
+      TypeError, () => builder.batchNormalization(inputFromOtherBuilder, mean, variance));
+}, '[batchNormalization] throw if input is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const meanFromOtherBuilder =
+      otherBuilder.input('mean', kExample1DTensorDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const variance = builder.input('variance', kExample1DTensorDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.batchNormalization(input, meanFromOtherBuilder, variance));
+}, '[batchNormalization] throw if mean is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const varianceFromOtherBuilder =
+      otherBuilder.input('variance', kExample1DTensorDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const mean = builder.input('mean', kExample1DTensorDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.batchNormalization(input, mean, varianceFromOtherBuilder));
+}, '[batchNormalization] throw if variance is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const scaleFromOtherBuilder =
+      otherBuilder.input('scale', kExample1DTensorDescriptor);
+  const options = {scale: scaleFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const mean = builder.input('mean', kExample1DTensorDescriptor);
+  const variance = builder.input('variance', kExample1DTensorDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.batchNormalization(input, mean, variance, options));
+}, '[batchNormalization] throw if scale option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const biasFromOtherBuilder =
+      otherBuilder.input('bias', kExample1DTensorDescriptor);
+  const options = {scale: biasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const mean = builder.input('mean', kExample1DTensorDescriptor);
+  const variance = builder.input('variance', kExample1DTensorDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.batchNormalization(input, mean, variance, options));
+}, '[batchNormalization] throw if bias option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const activationFromOtherBuilder = otherBuilder.clamp();
+  const options = {activation: activationFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const mean = builder.input('mean', kExample1DTensorDescriptor);
+  const variance = builder.input('variance', kExample1DTensorDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.batchNormalization(input, mean, variance, options));
+}, '[batchNormalization] throw if activation option is from another builder');