diff --git a/js/web/docs/webnn-operators.md b/js/web/docs/webnn-operators.md
index 7c2f10a034..f060109b31 100644
--- a/js/web/docs/webnn-operators.md
+++ b/js/web/docs/webnn-operators.md
@@ -6,7 +6,7 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
 
 (**Note**: ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'.)
 
-[WebNN API](https://webmachinelearning.github.io/webnn) provides two device types `cpu` and `gpu` to leverage different on-device accelerators. WebNN API implementation in Chromium uses XNNPack backend for `cpu` device type and DirectML backend for `gpu` device type. [The op support status](https://webmachinelearning.github.io/webnn-status/) behind these two backends is inconsistent since XNNPack backend currently supports limited op scope.
+[WebNN API](https://webmachinelearning.github.io/webnn) provides two device types `cpu` and `gpu` to leverage different on-device accelerators. WebNN API implementation in Chromium uses TFLite XNNPack delegate backend for `cpu` device type and DirectML backend for `gpu` device type. [The op support status](https://webmachinelearning.github.io/webnn-status/) behind these two backends is inconsistent.
 
 
 | Operator | Opset | WebNN API | WebNN CPU | WebNN GPU | Comments |
@@ -22,33 +22,33 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
 | Clip | ai.onnx(7-10, 11, 12, 13+) | clamp | ✓ | ✓ | WebNN CPU backend only supports 3 specific ranges: [0.0, infinity], [-1.0, 1.0], [0.0, 6.0] (Chromium issue: https://issues.chromium.org/issues/326156496) |
 | Concat | ai.onnx(7-10, 11-12, 13+) | concat | ✓ | ✓ | |
 | Conv | ai.onnx(7-10, 11+) | conv2d | ✓ | ✓ | Only supports 3-D or 4-D input and 'W' (weight). WebNN CPU requires the 'W' (weight) input to be a constant |
-| ConvTranspose | ai.onnx(7-10, 11+) | convTranspose2d | ✓ | ✗ | Only supports 3-D or 4-D input and 'W' (weight). |
-| Cos | ai.onnx(7+) | cos | ✗ | ✓ | |
+| ConvTranspose | ai.onnx(7-10, 11+) | convTranspose2d | ✗ | ✓ | Only supports 3-D or 4-D input and 'W' (weight). |
+| Cos | ai.onnx(7+) | cos | ✓ | ✓ | |
 | Div | ai.onnx(7-12, 13, 14+) | div | ✓ | ✓ | |
 | Elu | ai.onnx(7+) | elu | ✓ | ✓ | WebNN CPU backend only supports 'alpha' value is 1.0 |
-| Equal | ai.onnx(7-10, 11-12, 13-18, 19+) | equal | ✗ | ✓ | |
+| Equal | ai.onnx(7-10, 11-12, 13-18, 19+) | equal | ✓ | ✓ | |
 | Erf | ai.onnx(7-9, 10-12, 13+) | erf | ✗ | ✓ | |
-| Exp | ai.onnx(7-12, 13+) | exp | ✗ | ✓ | |
+| Exp | ai.onnx(7-12, 13+) | exp | ✓ | ✓ | |
 | Expand | ai.onnx(8-12, 13+) | expand | ✗ | ✓ | 'shape' input should be a constant |
 | Flatten | ai.onnx(7-8, 9-10, 11-12, 13-20, 21+) | reshape | ✓ | ✓ | |
 | Floor | ai.onnx(7-12, 13+) | floor | ✓ | ✓ | |
-| Gather | ai.onnx(7-10, 11-12, 13+) | gather | ✗ | ✓ | |
+| Gather | ai.onnx(7-10, 11-12, 13+) | gather | ✓ | ✓ | |
 | Gelu | ai.onnx(20+) | gelu | ✗ | ✓ | |
 | Gemm | ai.onnx(7-8, 9-10, 11-12, 13+) | gemm | ✓ | ✓ | Only supports 1-D 'C' input |
 | GlobalAveragePool | ai.onnx(7+) | averagePool2d | ✓ | ✓ | Only supports 4-D input |
 | GlobalMaxPool | ai.onnx(7+) | maxPool2d | ✓ | ✓ | Only supports 4-D input |
 | GlobalLpPool| ai.onnx(7+) | l2Pool2d | ✗ | ✓ | Only supports 4-D input, 'p' value is 2 |
-| Greater | ai.onnx(7-8, 9-12, 13+) | greater | ✗ | ✓ | |
-| GreaterOrEqual | ai.onnx(12-15, 16+) | greaterOrEqual | ✗ | ✓ | |
-| HardSigmoid | ai.onnx(7+) | hardSigmoid | ✗ | ✓ | |
+| Greater | ai.onnx(7-8, 9-12, 13+) | greater | ✓ | ✓ | |
+| GreaterOrEqual | ai.onnx(12-15, 16+) | greaterOrEqual | ✓ | ✓ | |
+| HardSigmoid | ai.onnx(7+) | hardSigmoid | ✓ | ✓ | |
 | HardSwish | ai.onnx(14+) | hardSwish | ✓ | ✓ | |
-| Identity | ai.onnx(7-13, 14-15, 16-18, 19-20, 21+) | identity | ✗ | ✓ | |
+| Identity | ai.onnx(7-13, 14-15, 16-18, 19-20, 21+) | identity | ✓ | ✓ | |
 | InstanceNormalization | ai.onnx(7+) | instanceNormalization | ✗ | ✓ | |
 | LayerNormalization | ai.onnx(7-16, 17+) | layerNormalization | ✗ | ✓ | |
 | LeakyRelu | ai.onnx(7-15, 16+) | leakyRelu | ✓ | ✓ | |
-| Less | ai.onnx(7-8, 9-12, 13+) | lesser | ✗ | ✓ | |
-| LessOrEqual | ai.onnx(12-15, 16+) | lesserOrEqual | ✗ | ✓ | |
-| Log | ai.onnx(7-12, 13+) | log | ✗ | ✓ | |
+| Less | ai.onnx(7-8, 9-12, 13+) | lesser | ✓ | ✓ | |
+| LessOrEqual | ai.onnx(12-15, 16+) | lesserOrEqual | ✓ | ✓ | |
+| Log | ai.onnx(7-12, 13+) | log | ✓ | ✓ | |
 | LpPool | ai.onnx(7-10, 11-17, 18+) | l2Pool2d | ✗ | ✓ | Only supports 4-D input, 2-D 'kernel_shape', 'p' value is 2 |
 | MatMul | ai.onnx(7-8, 9-12, 13+) | matmul | ✓ | ✓ | |
 | Max | ai.onnx(7, 8-11, 12, 13+) | max | ✓ | ✓ | |
@@ -56,38 +56,38 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
 | Min | ai.onnx(7, 8-11, 12, 13+) | min | ✓ | ✓ | |
 | Mul | ai.onnx(7-12, 13, 14+) | mul | ✓ | ✓ | |
 | Neg | ai.onnx(7-12, 13+) | neg | ✓ | ✓ | |
-| Not | ai.onnx(7+) | logicalnot | ✗ | ✓ | |
+| Not | ai.onnx(7+) | logicalnot | ✓ | ✓ | |
 | Pad | ai.onnx(7-10, 11-12, 13-17, 18, 19-20, 21+) | pad | ✓ | ✓ | modes == 'wrap' is not supported |
-| Pow | ai.onnx(7-11, 12, 13-14, 15+) | pow | ✗ | ✓ | |
+| Pow | ai.onnx(7-11, 12, 13-14, 15+) | pow | ✓ | ✓ | |
 | PRelu | ai.onnx(7-8, 9-15, 16+) | prelu | ✓ | ✓ | WebNN CPU restricts slope to be a static value |
 | Reciprocal | ai.onnx(7-12, 13+) | reciprocal | ✗ | ✓ | |
 | ReduceL1 | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceL1 | ✗ | ✓ | Input 'axes' if present should be a constant |
 | ReduceL2 | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceL2 | ✗ | ✓ | Input 'axes' if present should be a constant |
 | ReduceLogSum| ai.onnx(7-10, 11-12, 13-17, 18+) | reduceLogSum| ✗ | ✓ | Input 'axes' if present should be a constant |
 | ReduceLogSumExp | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceLogSumExp | ✗ | ✓ | Input 'axes' if present should be a constant |
-| ReduceMax | ai.onnx(7-10, 11, 12, 13-17, 18-19, 20+) | reduceMax | ✗ | ✓ | Input 'axes' if present should be a constant |
+| ReduceMax | ai.onnx(7-10, 11, 12, 13-17, 18-19, 20+) | reduceMax | ✓ | ✓ | Input 'axes' if present should be a constant |
 | ReduceMean | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceMean | ✓ | ✓ | Input 'axes' if present should be a constant |
-| ReduceMin | ai.onnx(7-10, 11, 12, 13-17, 18-19, 20+) | reduceMin | ✗ | ✓ | Input 'axes' if present should be a constant |
-| ReduceProd | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceProduct | ✗ | ✓ | Input 'axes' if present should be a constant |
-| ReduceSum | ai.onnx(7-10, 11-12, 13+) | reduceSum | ✗ | ✓ | Input 'axes' if present should be a constant |
+| ReduceMin | ai.onnx(7-10, 11, 12, 13-17, 18-19, 20+) | reduceMin | ✓ | ✓ | Input 'axes' if present should be a constant |
+| ReduceProd | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceProduct | ✓ | ✓ | Input 'axes' if present should be a constant |
+| ReduceSum | ai.onnx(7-10, 11-12, 13+) | reduceSum | ✓ | ✓ | Input 'axes' if present should be a constant |
 | ReduceSumSquare | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceSumSquare | ✗ | ✓ | Input 'axes' if present should be a constant |
 | Relu | ai.onnx(7-12, 13, 14+) | relu | ✓ | ✓ | |
 | Reshape | ai.onnx(7-12, 13, 14-18, 19-20, 21+) | reshape | ✓ | ✓ | Input 'shape' should be a constant, 0 dimension value in 'shape' is not supported |
 | Resize | ai.onnx(11-12, 13-17, 18, 19+) | resample2d | ✓ | ✓ | Only supports 4-D input, exclude_outside != 0, input 'scales' and 'sizes' if present must be a constant, 'linear' and 'nearest' modes |
 | Shape | ai.onnx(7-12, 13-14, 15-18, 19-20, 21+) | slice | ✓ | ✓ | |
 | Sigmoid | ai.onnx(7-12, 13+) | sigmoid | ✓ | ✓ | |
-| Softplus | ai.onnx(7+) | softplus | ✗ | ✓ | |
+| Softplus | ai.onnx(7+) | softplus | ✓ | ✓ | |
 | Softsign | ai.onnx(7+) | softsign | ✗ | ✓ | |
-| Sin | ai.onnx(7+) | sin | ✗ | ✓ | |
+| Sin | ai.onnx(7+) | sin | ✓ | ✓ | |
 | Slice | ai.onnx(7-9, 10, 11-12, 13+) | slice | ✓ | ✓ | Input 'starts', 'ends', 'axes', and 'steps' if present must be a constant, only supports 'steps' value 1 |
 | Softmax | ai.onnx(7-10, 11-12, 13+) | softmax | ✓ | ✓ | |
 | Split | ai.onnx(7-10, 11-12, 13-17, 18+) | split | ✓ | ✓ | Input 'split' if present should be a constant |
 | Sqrt | ai.onnx(7-12, 13+) | sqrt | ✓ | ✓ | |
 | Squeeze | ai.onnx(7-10, 11-12, 13-20, 21+) | reshape | ✓ | ✓ | Input 'axes' if present should be a constant |
 | Sub | ai.onnx(7-12, 13, 14+) | sub | ✓ | ✓ | |
-| Tan | ai.onnx(7+) | tan | ✗ | ✓ | |
+| Tan | ai.onnx(7+) | tan | ✓ | ✓ | |
 | Tanh | ai.onnx(7-12, 13+) | tanh | ✓ | ✓ | |
 | Transpose | ai.onnx(7-12, 13-20, 21+) | transpose | ✓ | ✓ | |
 | Trilu | ai.onnx(14+) | triangular | ✗ | ✓ | Input 'k' (option 'diagonal' for WebNN) if present should be a constant |
 | Unsqueeze | ai.onnx(7-10, 11-12, 13-20, 21+) | reshape | ✓ | ✓ | |
-| Where | ai.onnx(7-8, 9-15, 16+) | where | ✗ | ✓ | |
+| Where | ai.onnx(7-8, 9-15, 16+) | where | ✓ | ✓ | |
diff --git a/onnxruntime/core/providers/webnn/builders/helper.h b/onnxruntime/core/providers/webnn/builders/helper.h
index 7c84a14ba7..daecfcf457 100644
--- a/onnxruntime/core/providers/webnn/builders/helper.h
+++ b/onnxruntime/core/providers/webnn/builders/helper.h
@@ -168,34 +168,34 @@ static const InlinedHashMap<std::string, WebnnOpInfo> op_map = {
     {"Conv", {"conv2d", true}},
     {"ConvInteger", {"conv2dInteger", false}},
     {"ConvTranspose", {"convTranspose2d", false}},
-    {"Cos", {"cos", false}},
+    {"Cos", {"cos", true}},
     {"Div", {"div", true}},
     {"DequantizeLinear", {"dequantizeLinear", false}},
     {"DynamicQuantizeLinear", {"dynamicQuantizeLinear", false}},
     {"Elu", {"elu", true}},
-    {"Equal", {"equal", false}},
+    {"Equal", {"equal", true}},
     {"Erf", {"erf", false}},
-    {"Exp", {"exp", false}},
+    {"Exp", {"exp", true}},
     {"Expand", {"expand", false}},
     {"Flatten", {"reshape", true}},
     {"Floor", {"floor", true}},
-    {"Gather", {"gather", false}},
+    {"Gather", {"gather", true}},
     {"Gelu", {"gelu", false}},
     {"Gemm", {"gemm", true}},
     {"GlobalAveragePool", {"averagePool2d", true}},
     {"GlobalMaxPool", {"maxPool2d", true}},
     {"GlobalLpPool", {"l2Pool2d", false}},
-    {"Greater", {"greater", false}},
-    {"GreaterOrEqual", {"greaterOrEqual", false}},
-    {"HardSigmoid", {"hardSigmoid", false}},
+    {"Greater", {"greater", true}},
+    {"GreaterOrEqual", {"greaterOrEqual", true}},
+    {"HardSigmoid", {"hardSigmoid", true}},
     {"HardSwish", {"hardSwish", true}},
-    {"Identity", {"identity", false}},
+    {"Identity", {"identity", true}},
     {"InstanceNormalization", {"instanceNormalization", false}},
     {"LayerNormalization", {"layerNormalization", false}},
     {"LeakyRelu", {"leakyRelu", true}},
-    {"Less", {"lesser", false}},
-    {"LessOrEqual", {"lesserOrEqual", false}},
-    {"Log", {"log", false}},
+    {"Less", {"lesser", true}},
+    {"LessOrEqual", {"lesserOrEqual", true}},
+    {"Log", {"log", true}},
     {"LpPool", {"l2Pool2d", false}},
     {"MatMul", {"matmul", true}},
     {"MatMulInteger", {"matmulInteger", false}},
@@ -204,41 +204,41 @@ static const InlinedHashMap<std::string, WebnnOpInfo> op_map = {
     {"Min", {"min", true}},
     {"Mul", {"mul", true}},
     {"Neg", {"neg", true}},
-    {"Not", {"logicalNot", false}},
+    {"Not", {"logicalNot", true}},
     {"Pad", {"pad", true}},
-    {"Pow", {"pow", false}},
+    {"Pow", {"pow", true}},
     {"PRelu", {"prelu", true}},
     {"Reciprocal", {"reciprocal", false}},
     {"ReduceL1", {"reduceL1", false}},
     {"ReduceL2", {"reduceL2", false}},
     {"ReduceLogSum", {"reduceLogSum", false}},
     {"ReduceLogSumExp", {"reduceLogSumExp", false}},
-    {"ReduceMax", {"reduceMax", false}},
+    {"ReduceMax", {"reduceMax", true}},
     {"ReduceMean", {"reduceMean", true}},
-    {"ReduceMin", {"reduceMin", false}},
-    {"ReduceProd", {"reduceProduct", false}},
-    {"ReduceSum", {"reduceSum", false}},
+    {"ReduceMin", {"reduceMin", true}},
+    {"ReduceProd", {"reduceProduct", true}},
+    {"ReduceSum", {"reduceSum", true}},
     {"ReduceSumSquare", {"reduceSumSquare", false}},
     {"Relu", {"relu", true}},
     {"Reshape", {"reshape", true}},
     {"Resize", {"resample2d", true}},
     {"Shape", {"slice", true}},
     {"Sigmoid", {"sigmoid", true}},
-    {"Softplus", {"softplus", false}},
+    {"Softplus", {"softplus", true}},
     {"Softsign", {"softsign", false}},
-    {"Sin", {"sin", false}},
+    {"Sin", {"sin", true}},
     {"Slice", {"slice", true}},
     {"Softmax", {"softmax", true}},
     {"Split", {"split", true}},
     {"Sqrt", {"sqrt", true}},
     {"Squeeze", {"reshape", true}},
     {"Sub", {"sub", true}},
-    {"Tan", {"tan", false}},
+    {"Tan", {"tan", true}},
     {"Tanh", {"tanh", true}},
     {"Transpose", {"transpose", true}},
     {"Trilu", {"triangular", false}},
     {"Unsqueeze", {"reshape", true}},
-    {"Where", {"where", false}},
+    {"Where", {"where", true}},
 };
 
 inline bool CheckSingleOp(const std::string& op_type, const emscripten::val& wnn_builder_,
@@ -251,10 +251,10 @@ inline bool CheckSingleOp(const std::string& op_type, const emscripten::val& wnn
   if (!wnn_builder_[op_map.find(op_type)->second.opName].as<bool>()) {
     return false;
   }
-  // The current WebNN CPU (XNNPack) backend supports a limited op list, and we'd rather
+  // The current WebNN CPU (TFLite) backend supports a limited op list, and we'd rather
   // fall back early to the ORT CPU EP rather than fail in the WebNN "cpu" deviceType.
   // This is a workaround because the op may be included in MLGraphBuilder for DirectML
-  // backend but without XNNPack implementation in Chromium.
+  // backend but without TFLite implementation in Chromium.
   if (!op_map.find(op_type)->second.isCpuSupported && device_type == WebnnDeviceType::CPU) {
     return false;
   }