diff --git a/backends/qualcomm/builders/op_arange.py b/backends/qualcomm/builders/op_arange.py
index 0a95d55dca3..882aae24ef1 100644
--- a/backends/qualcomm/builders/op_arange.py
+++ b/backends/qualcomm/builders/op_arange.py
@@ -6,7 +6,6 @@
 from typing import Dict
 
 import executorch.backends.qualcomm.python.PyQnnManagerAdaptor as PyQnnManager
-
 import torch
 
 from .node_visitor import NodeVisitor
@@ -27,7 +26,8 @@ def define_node(
     ) -> PyQnnManager.PyQnnOpWrapper:
         start, end = node.args[0:2]
         step = node.args[2] if len(node.args) > 2 else 1
-        out_tensor = torch.arange(start, end, step)
+        dtype = node.kwargs.get("dtype")
+        out_tensor = torch.arange(start, end, step, dtype=dtype)
 
         # since we can derive the constant value of current op in AoT stage
         # we only build static tensor here for consumers of current node
diff --git a/backends/qualcomm/tests/models.py b/backends/qualcomm/tests/models.py
index 6d4c9eac466..da146818e38 100644
--- a/backends/qualcomm/tests/models.py
+++ b/backends/qualcomm/tests/models.py
@@ -177,7 +177,7 @@ def forward(self, x):
 
 
 class Arange(torch.nn.Module):
-    def __init__(self, start, end, step, dtype):
+    def __init__(self, start, end, step, dtype=None):
         super().__init__()
         self.start = start
         self.end = end
diff --git a/backends/qualcomm/tests/test_passes.py b/backends/qualcomm/tests/test_passes.py
index 8af66c4cbef..cd69a2428c5 100644
--- a/backends/qualcomm/tests/test_passes.py
+++ b/backends/qualcomm/tests/test_passes.py
@@ -7,7 +7,6 @@
     InsertReshapeForReduceOps,
     RemoveRedundancy,
 )
-
 from executorch.exir import to_edge
 from executorch.exir.dialects._ops import ops as exir_ops
 
@@ -149,6 +148,91 @@ def test_mha_to_sha(self):
                 f"Output {i} mismatch: got {out}, expected {ref}",
             )
 
+    def test_arange_dtype_from_kwargs(self):
+        """Test that op_arange builder respects dtype from node kwargs."""
+        from collections import defaultdict
+
+        import executorch.backends.qualcomm.python.PyQnnManagerAdaptor as PyQnnManager
+        from executorch.backends.qualcomm._passes.qnn_pass_manager import QnnPassManager
+        from executorch.backends.qualcomm.builders.node_visitor_manager import (
+            get_node_visitors,
+        )
+        from executorch.backends.qualcomm.utils.utils import capture_program
+
+        class ArangeWithDtype(torch.nn.Module):
+            def __init__(self, dtype):
+                super().__init__()
+                self.dtype = dtype
+
+            def forward(self, x):
+                return torch.arange(0, 10, 1, dtype=self.dtype) + x
+
+        # Map torch dtypes to expected QNN data types
+        dtype_to_qnn = {
+            torch.float32: PyQnnManager.Qnn_DataType_t.QNN_DATATYPE_FLOAT_32,
+            torch.int64: PyQnnManager.Qnn_DataType_t.QNN_DATATYPE_INT_64,
+            torch.int32: PyQnnManager.Qnn_DataType_t.QNN_DATATYPE_INT_32,
+        }
+
+        test_cases = [
+            (torch.float32, dtype_to_qnn[torch.float32]),
+            (torch.int64, dtype_to_qnn[torch.int64]),
+            (torch.int32, dtype_to_qnn[torch.int32]),
+            (None, dtype_to_qnn[torch.int64]),  # None uses torch default (int64)
+        ]
+
+        for input_dtype, expected_qnn_dtype in test_cases:
+            with self.subTest(input_dtype=input_dtype):
+                mod = ArangeWithDtype(input_dtype)
+                # Use appropriate input tensor dtype
+                if input_dtype == torch.float32:
+                    x = torch.zeros(10, dtype=torch.float32)
+                else:
+                    x = torch.zeros(10, dtype=torch.int64)
+
+                sample_input = (x,)
+                delegated_program = capture_program(mod, sample_input)
+
+                # Transform graph through QNN pass manager
+                graph_module = QnnPassManager().transform_for_preprocess_pipeline(
+                    delegated_program.exported_program
+                )
+
+                # Get node visitors (builders)
+                nodes_to_wrappers = defaultdict(dict)
+                node_visitors = get_node_visitors(
+                    delegated_program.exported_program, enable_tensor_dump=False
+                )
+
+                # Find and process the arange node through the builder
+                for node in graph_module.graph.nodes:
+                    if node.op == "call_function" and "arange" in node.target.__name__:
+                        # Invoke the Arange builder's define_node
+                        node_visitors[node.target.__name__].define_node(
+                            node, nodes_to_wrappers
+                        )
+
+                        # Check that a tensor wrapper was created
+                        self.assertIn(
+                            node.name,
+                            nodes_to_wrappers,
+                            f"No tensor wrapper created for arange node",
+                        )
+
+                        # Get the tensor wrapper and verify its dtype
+                        tensor_wrapper = nodes_to_wrappers[node.name][0]
+                        actual_dtype = tensor_wrapper.GetDataType()
+
+                        self.assertEqual(
+                            actual_dtype,
+                            expected_qnn_dtype,
+                            f"QNN dtype mismatch for input_dtype={input_dtype}: "
+                            f"got {actual_dtype}, expected {expected_qnn_dtype}",
+                        )
+                        break
+                else:
+                    self.fail("No arange node found in graph")
+
 
 if __name__ == "__main__":
     unittest.main()