diff --git a/src/Compiler/OnnxToMlirPasses.cpp b/src/Compiler/OnnxToMlirPasses.cpp
index 291ca8e464..ccba81eb6c 100644
--- a/src/Compiler/OnnxToMlirPasses.cpp
+++ b/src/Compiler/OnnxToMlirPasses.cpp
@@ -97,6 +97,9 @@ void addONNXToMLIRPasses(mlir::PassManager &pm, bool targetCPU,
   if (opts.enableRemoveDqQOp)
     pm.addPass(createQDQOptONNXToONNXPass());
 
+  if (opts.enableAddQDQOp)
+    pm.addPass(createMissingQDQAroundOpOptONNXToONNXPass());
+
   // One more call to ONNX shape inference/canonicalization/... to update
   // shape if possible.
   if (opts.enableONNXHybridPass) {
diff --git a/src/Compiler/OnnxToMlirPasses.hpp b/src/Compiler/OnnxToMlirPasses.hpp
index a7532ec926..6c2d0a7b4a 100644
--- a/src/Compiler/OnnxToMlirPasses.hpp
+++ b/src/Compiler/OnnxToMlirPasses.hpp
@@ -19,6 +19,7 @@ struct OnnxToMlirOptions {
   bool enableRemoveDqQOp = true;
   bool enableRemoveDqQAroundOp = true;
   bool enableRemoveBinary = false;
+  bool enableAddQDQOp = false;
 
   bool disableRecomposeOption = false;
   bool enableONNXHybridPass = true;
diff --git a/src/Dialect/ONNX/Transforms/AddQDQOpt.cpp b/src/Dialect/ONNX/Transforms/AddQDQOpt.cpp
new file mode 100644
index 0000000000..25685e7657
--- /dev/null
+++ b/src/Dialect/ONNX/Transforms/AddQDQOpt.cpp
@@ -0,0 +1,187 @@
+
+// (c) Copyright 2022 - 2025 Advanced Micro Devices, Inc. All Rights Reserved.
+//
+//===----------------------------------------------------------------------===//
+
+#include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/IR/BuiltinAttributes.h"
+#include "mlir/IR/BuiltinTypes.h"
+#include "mlir/IR/Operation.h"
+#include "mlir/IR/PatternMatch.h"
+#include "mlir/IR/Value.h"
+#include "mlir/Pass/Pass.h"
+#include "mlir/Transforms/DialectConversion.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
+#include "mlir/Transforms/WalkPatternRewriteDriver.h"
+#include "src/Dialect/ONNX/ONNXOps.hpp"
+#include "src/Dialect/ONNX/ONNXOps/OpHelper.hpp"
+#include "src/Pass/Passes.hpp"
+#include "llvm/ADT/STLExtras.h"
+#include <cmath>
+#include <optional>
+#include <variant>
+
+#include "mlir/IR/Builders.h"
+#include "mlir/IR/BuiltinOps.h"
+#include "src/Dialect/ONNX/ONNXOps.hpp"
+
+using namespace mlir;
+using namespace onnx_mlir;
+
+namespace {
+
+struct AddQDQAroundOp
+    : public PassWrapper<AddQDQAroundOp, OperationPass<func::FuncOp>> {
+
+  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(AddQDQAroundOp)
+
+  StringRef getArgument() const final { return "add-qdq-around-op"; }
+  StringRef getDescription() const final {
+    return "Add Q ,DQ around nodes which are missing them";
+  }
+
+  Type extractZeroPointType(OpBuilder &builder, func::FuncOp &func) {
+    Type zpElemType = builder.getIntegerType(8); // default int8
+
+    func.walk([&](Operation *op) -> WalkResult {
+      if (auto q = mlir::dyn_cast<ONNXQuantizeLinearOp>(op)) {
+        Value zp = q.getYZeroPoint();
+        if (auto zpShaped = llvm::dyn_cast<ShapedType>(zp.getType())) {
+          zpElemType = zpShaped.getElementType();
+          return WalkResult::interrupt();
+        }
+      } else if (auto dq = mlir::dyn_cast<ONNXDequantizeLinearOp>(op)) {
+        Value zp = dq.getXZeroPoint();
+        if (auto zpShaped = llvm::dyn_cast<ShapedType>(zp.getType())) {
+          zpElemType = zpShaped.getElementType();
+          return WalkResult::interrupt();
+        }
+      }
+      return WalkResult::advance();
+    });
+    return zpElemType;
+  }
+
+  bool isDMAOp(Operation *op) {
+    return isa<ONNXReshapeOp, ONNXFlattenOp, ONNXSqueezeOp, ONNXUnsqueezeOp,
+        ONNXExpandOp, ONNXTransposeOp, ONNXIdentityOp, ONNXSliceOp,
+        ONNXConcatOp, ONNXSplitOp, ONNXGatherOp, ONNXGatherElementsOp,
+        ONNXGatherNDOp, ONNXScatterOp, ONNXScatterElementsOp, ONNXScatterNDOp,
+        ONNXPadOp, ONNXCastOp, ONNXShapeOp, ONNXConstantOfShapeOp, ONNXTileOp,
+        ONNXDepthToSpaceOp, ONNXSpaceToDepthOp, ONNXResizeOp>(op);
+  }
+
+  void runOnOperation() override {
+    func::FuncOp func = getOperation();
+    MLIRContext *ctx = &getContext();
+    OpBuilder builder(ctx);
+
+    Block &entryBlock = func.getBody().front();
+    Location entryLoc = func.getLoc();
+
+    RankedTensorType scaleType =
+        RankedTensorType::get({}, builder.getF32Type());
+    DenseElementsAttr scaleAttr = DenseElementsAttr::get(scaleType, {1.0f});
+    OperationState scaleState(entryLoc, "onnx.Constant");
+    scaleState.addAttribute("value", scaleAttr);
+    scaleState.addTypes(scaleAttr.getType());
+    Operation *scaleOp = Operation::create(scaleState);
+    entryBlock.getOperations().insert(entryBlock.begin(), scaleOp);
+    Value scaleVal = scaleOp->getResult(0);
+
+    Type zpEleType = extractZeroPointType(builder, func);
+    RankedTensorType zpType = RankedTensorType::get({}, zpEleType);
+    DenseElementsAttr zpAttr;
+    if (isa<IntegerType>(zpEleType)) {
+      auto intType = mlir::dyn_cast<IntegerType>(zpEleType);
+      unsigned width = intType.getWidth();
+      bool isSigned = intType.isSignedInteger();
+
+      if (width == 8) {
+        if (isSigned) {
+          zpAttr = DenseElementsAttr::get(zpType, {static_cast<int8_t>(0)});
+        } else {
+          zpAttr = DenseElementsAttr::get(zpType, {static_cast<uint8_t>(0)});
+        }
+      } else if (width == 16) {
+        if (isSigned) {
+          zpAttr = DenseElementsAttr::get(zpType, {static_cast<int16_t>(0)});
+        } else {
+          zpAttr = DenseElementsAttr::get(zpType, {static_cast<uint16_t>(0)});
+        }
+      } else if (width == 32) {
+        if (isSigned) {
+          zpAttr = DenseElementsAttr::get(zpType, {static_cast<int32_t>(0)});
+        } else {
+          zpAttr = DenseElementsAttr::get(zpType, {static_cast<uint32_t>(0)});
+        }
+      } else {
+        // fallback: default int8 zero-point
+        zpAttr = DenseElementsAttr::get(zpType, {static_cast<int8_t>(0)});
+      }
+    } else {
+      // fallback if not integer
+      zpAttr = DenseElementsAttr::get(zpType, {static_cast<int8_t>(0)});
+    }
+
+    OperationState zpState(entryLoc, "onnx.Constant");
+    zpState.addAttribute("value", zpAttr);
+    zpState.addTypes(zpAttr.getType());
+    Operation *zpOp = Operation::create(zpState);
+    entryBlock.getOperations().insert(entryBlock.begin(), zpOp);
+    Value zpVal = zpOp->getResult(0);
+
+    llvm::SmallDenseMap<Value, Value> producerToDQ;
+
+    for (Operation &opRef : llvm::make_early_inc_range(func.getOps())) {
+      Operation *op = &opRef;
+
+      if ((isa<ONNXConstantOp, ONNXQuantizeLinearOp, ONNXDequantizeLinearOp>(
+              op)) ||
+          !(isDMAOp(op)))
+        continue;
+
+      Location loc = op->getLoc();
+
+      for (Value operand : op->getOperands()) {
+        Operation *def = operand.getDefiningOp();
+        if (def && isa<ONNXQuantizeLinearOp, ONNXDequantizeLinearOp>(def))
+          continue;
+
+        auto it = producerToDQ.find(operand);
+        if (it != producerToDQ.end()) {
+          op->replaceUsesOfWith(operand, it->second);
+          continue;
+        }
+
+        builder.setInsertionPoint(op);
+
+        ShapedType inShaped = llvm::dyn_cast<ShapedType>(operand.getType());
+
+        Type qResultType = operand.getType();
+        if (isa<FloatType>(operand.getType()) ||
+            (inShaped && isa<FloatType>(inShaped.getElementType()))) {
+          if (inShaped)
+            qResultType = RankedTensorType::get(inShaped.getShape(), zpEleType);
+          else
+            qResultType = RankedTensorType::get({}, zpEleType);
+
+          auto q = builder.create<ONNXQuantizeLinearOp>(
+              loc, qResultType, operand, scaleVal, zpVal);
+          auto dq = builder.create<ONNXDequantizeLinearOp>(
+              loc, operand.getType(), q.getResult(), scaleVal, zpVal);
+
+          producerToDQ.try_emplace(operand, dq.getResult());
+          op->replaceUsesOfWith(operand, dq.getResult());
+        }
+      }
+    }
+  }
+};
+} // namespace
+
+namespace onnx_mlir {
+std::unique_ptr<mlir::Pass> createMissingQDQAroundOpOptONNXToONNXPass() {
+  return std::make_unique<AddQDQAroundOp>();
+}
+} // namespace onnx_mlir
\ No newline at end of file
diff --git a/src/Dialect/ONNX/Transforms/CMakeLists.txt b/src/Dialect/ONNX/Transforms/CMakeLists.txt
index 7862443935..2ba5887acc 100644
--- a/src/Dialect/ONNX/Transforms/CMakeLists.txt
+++ b/src/Dialect/ONNX/Transforms/CMakeLists.txt
@@ -44,7 +44,8 @@ add_onnx_mlir_library(OMONNXRewrite
   ConstProp.cpp
   QDQAroundOpOpt.cpp    
   QDQOpt.cpp
-  DQBinaryQOpt.cpp  
+  DQBinaryQOpt.cpp 
+  AddQDQOpt.cpp
   ConvOpt.cpp
   Decompose.cpp
   DecomposeEinsum.cpp
diff --git a/src/Pass/Passes.hpp b/src/Pass/Passes.hpp
index 8e4855e6dc..f0420d4b63 100644
--- a/src/Pass/Passes.hpp
+++ b/src/Pass/Passes.hpp
@@ -58,6 +58,7 @@ std::unique_ptr<mlir::Pass> createQDQAroundOpOptONNXToONNXPass();
 
 std::unique_ptr<mlir::Pass> createQDQOptONNXToONNXPass();
 std::unique_ptr<mlir::Pass> createFoldDQBinaryQPass();
+std::unique_ptr<mlir::Pass> createMissingQDQAroundOpOptONNXToONNXPass();
 
 /// Pass for instrument the ops in specific stage.
 std::unique_ptr<mlir::Pass> createInstrumentPass();
diff --git a/src/Tools/onnx-mlir-opt/RegisterPasses.cpp b/src/Tools/onnx-mlir-opt/RegisterPasses.cpp
index ba6635b466..50fd89cb80 100644
--- a/src/Tools/onnx-mlir-opt/RegisterPasses.cpp
+++ b/src/Tools/onnx-mlir-opt/RegisterPasses.cpp
@@ -83,6 +83,10 @@ void registerOMPasses(int optLevel) {
     return createFoldDQBinaryQPass();
   });
 
+  mlir::registerPass([]() -> std::unique_ptr<mlir::Pass> {
+    return createMissingQDQAroundOpOptONNXToONNXPass();
+  });
+
   mlir::registerPass(
       []() -> std::unique_ptr<mlir::Pass> { return createInstrumentPass(); });
 
diff --git a/test/mlir/onnx/onnx_add_qdq.mlir b/test/mlir/onnx/onnx_add_qdq.mlir
new file mode 100644
index 0000000000..8a0c63ee19
--- /dev/null
+++ b/test/mlir/onnx/onnx_add_qdq.mlir
@@ -0,0 +1,33 @@
+// RUN: onnx-mlir-opt --add-qdq-around-op %s | FileCheck %s
+ 
+func.func @test_inserted_qdq(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
+  %shape = onnx.Constant {value = dense<[2, 2]> : tensor<2xi64>} : tensor<2xi64>
+  %cst = onnx.Constant {value = dense<1.0> : tensor<2x2xf32>} : tensor<2x2xf32>
+  %init = onnx.Constant dense<2.0> : tensor<f32>
+  %cst_qdq_zp = onnx.Constant dense<0> : tensor<i16>
+  %cst_qdq_s = onnx.Constant dense<1.52590219E-5> : tensor<f32>
+  %0 = "onnx.Add"(%arg0, %cst) : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
+  %1 = "onnx.Mul"(%0, %init) : (tensor<2x2xf32>, tensor<f32>) -> tensor<2x2xf32>
+  %2 =  "onnx.Reshape"(%1, %shape) : (tensor<2x2xf32>, tensor<2xi64>) -> tensor<2x2xf32>
+  %3 = "onnx.QuantizeLinear"(%0, %cst_qdq_s, %cst_qdq_zp) {axis = 1 : si64, block_size = 0 : si64, output_dtype = 0 : si64, saturate = 1 : si64} : (tensor<2x2xf32>, tensor<f32>, tensor<i16>) -> tensor<2x2xi16>
+  %4 = "onnx.DequantizeLinear"(%3, %cst_qdq_s, %cst_qdq_zp) {axis = 1 : si64, block_size = 0 : si64} : (tensor<2x2xi16>, tensor<f32>, tensor<i16>) -> tensor<2x2xf32> 
+  %5 = "onnx.Reshape"(%4, %shape) : (tensor<2x2xf32>, tensor<2xi64>) -> tensor<2x2xf32>
+  %6 = "onnx.Transpose"(%0) {perm = [0, 1]} : (tensor<2x2xf32>) -> tensor<2x2xf32>
+  %7 = "onnx.Add"(%5, %6) : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
+
+  return %7 : tensor<2x2xf32>
+}
+
+// CHECK-LABEL: func.func @test_inserted_qdq
+// CHECK: onnx.Add
+// CHECK: onnx.Mul
+// CHECK: onnx.QuantizeLinear
+// CHECK: onnx.DequantizeLinear
+// CHECK: onnx.Reshape
+// CHECK: onnx.QuantizeLinear
+// CHECK: onnx.DequantizeLinear
+// CHECK: onnx.Reshape
+// CHECK: onnx.QuantizeLinear
+// CHECK: onnx.DequantizeLinear
+// CHECK: onnx.Transpose
+// CHECK: onnx.Add