diff --git a/tensorflow/lite/delegates/webnn/webnn_delegate.cc b/tensorflow/lite/delegates/webnn/webnn_delegate.cc
index c01dbb025184ea..7cb89c57e9353e 100644
--- a/tensorflow/lite/delegates/webnn/webnn_delegate.cc
+++ b/tensorflow/lite/delegates/webnn/webnn_delegate.cc
@@ -315,6 +315,15 @@ class Subgraph {
       }
     }
 
+
+    emscripten::val graph_inputs = emscripten::val::object();
+    for (auto& i : compute_inputs) {
+      std::string name = std::to_string(i);
+      auto input_size = context->tensors[i].bytes / 4;
+      graph_inputs.set(name, emscripten::val::global("Float32Array").new_(input_size));
+    }
+
+    emscripten::val graph_outputs = emscripten::val::object();
     emscripten::val named_operands = emscripten::val::object();
     for (auto o : outputs) {
       std::string name = std::to_string(o);
@@ -322,15 +331,19 @@ class Subgraph {
         TF_LITE_KERNEL_LOG(context, "Invalid operand");
         return nullptr;
       }
+      auto output_size = context->tensors[o].bytes / 4;
+      graph_outputs.set(name, emscripten::val::global("Float32Array").new_(output_size));
       named_operands.set(name, webnn_operands.at(o));
     }
 
-    emscripten::val wnn_graph = wnn_builder.call<emscripten::val>("buildSync", named_operands);
+    emscripten::val wnn_graph = wnn_builder.call<emscripten::val>("build", named_operands).await();
     if (!wnn_graph.as<bool>()) {
       TF_LITE_KERNEL_LOG(context, "failed to build WebNN graph");
       return nullptr;
     }
-    return new Subgraph(delegate->wnn_context_, wnn_graph, std::move(compute_inputs), std::move(outputs));
+    return new Subgraph(delegate->wnn_context_, wnn_graph,
+                        std::move(compute_inputs), std::move(outputs),
+                        graph_inputs, graph_outputs);
   }
 
   TfLiteStatus Prepare(TfLiteContext* context) { return kTfLiteOk; }
@@ -356,27 +369,43 @@ class Subgraph {
       }
     }
 
+    std::unordered_map<std::string, emscripten::val> output_views;
     if (any_pointers_changed) {
-      graph_inputs_ = emscripten::val::object();
       for (int t : inputs_) {
         std::string name = std::to_string(t);
         auto input_size = context->tensors[t].bytes / 4;
         auto input_data = context->tensors[t].data.f;
         emscripten::val view{ emscripten::typed_memory_view(input_size, input_data) };
-        graph_inputs_.set(name, view);
+        graph_inputs_[name].call<void>("set", view);
       }
 
-      graph_outputs_ = emscripten::val::object();
       for (int t : outputs_) {
         std::string name = std::to_string(t);
         auto output_size = context->tensors[t].bytes / 4;
         auto output_data = context->tensors[t].data.f;
         emscripten::val view{emscripten::typed_memory_view(output_size, output_data)};
-        graph_outputs_.set(name, view);
+        output_views.insert({name, view});
+      }
+    }
+
+    const emscripten::val results =
+        wnn_context_
+            .call<emscripten::val>("compute", wnn_graph_, graph_inputs_, graph_outputs_)
+            .await();
+
+    if (any_pointers_changed) {
+      // Copy the outputs from pre-allocated ArrayBuffers back to the Wasm ArrayBuffer.
+      for (int t : outputs_) {
+        std::string name = std::to_string(t);
+        emscripten::val view = output_views.at(name);
+        view.call<void>("set", results["outputs"][name]);
       }
     }
 
-    wnn_context_.call<void>("computeSync", wnn_graph_, graph_inputs_, graph_outputs_);
+    // WebNN compute() method would return the input and output buffers via the
+    // promise resolution. Reuse the buffers to avoid additional allocation.
+    graph_inputs_ = results["inputs"];
+    graph_outputs_ = results["outputs"];
 
     return kTfLiteOk;
   }
@@ -3692,16 +3721,21 @@ class Subgraph {
   }
 
  private:
-  Subgraph(emscripten::val context, emscripten::val graph, std::unordered_set<int>&& inputs, std::unordered_set<int>&& outputs)
-      : wnn_context_(context), wnn_graph_(graph), inputs_(inputs), outputs_(outputs) {
+  Subgraph(emscripten::val context, emscripten::val graph,
+           std::unordered_set<int>&& inputs, std::unordered_set<int>&& outputs,
+           emscripten::val graph_inputs, emscripten::val graph_outputs)
+      : wnn_context_(context),
+        wnn_graph_(graph),
+        inputs_(inputs),
+        outputs_(outputs),
+        graph_inputs_(graph_inputs),
+        graph_outputs_(graph_outputs) {
     for (auto& i : inputs_) {
       externals_[i] = nullptr;
     }
     for (auto& o : outputs_) {
       externals_[o] = nullptr;
     }
-    graph_inputs_ = emscripten::val::object();
-    graph_outputs_ = emscripten::val::object();
   }
 
   emscripten::val wnn_context_ = emscripten::val::object();
@@ -4147,7 +4181,7 @@ TfLiteDelegate* TfLiteWebNNDelegateCreate(
                       emscripten::val(power_preference_name));
   context_options.set("numThreads", options->numThreads);
   emscripten::val wnn_context =
-      ml.call<emscripten::val>("createContextSync", context_options);
+      ml.call<emscripten::val>("createContext", context_options).await();
 
   if (!wnn_context.as<bool>()) {
     TFLITE_LOG_PROD_ONCE(tflite::TFLITE_LOG_ERROR,