diff --git a/circle-mlir/circle-mlir/lib/dialect/mlir/CircleOps.td b/circle-mlir/circle-mlir/lib/dialect/mlir/CircleOps.td
index 99f30bc7cf0..eaa0abfebd0 100644
--- a/circle-mlir/circle-mlir/lib/dialect/mlir/CircleOps.td
+++ b/circle-mlir/circle-mlir/lib/dialect/mlir/CircleOps.td
@@ -1619,6 +1619,32 @@ def CIR_PReluOp : CIR_Op<"prelu", [
   */
 }
 
+def CIR_RangeOp : CIR_Op<"range", [
+    Pure,
+    CIR_OperandHasRank<0, 0>,
+    CIR_OperandHasRank<1, 0>,
+    CIR_OperandHasRank<2, 0>,
+    PredOpTrait<"operands and output must have same element type",
+      And<[TCresVTEtIsSameAsOp<0, 0>, TCresVTEtIsSameAsOp<0, 1>,
+           TCresVTEtIsSameAsOp<0, 2>]>>]> {
+  let summary = "Range operator";
+
+  let description = [{
+    Returns a 1D tensor defined by a sequence from `start` to `limit` with
+    a given `delta`.
+  }];
+
+  let arguments = (ins
+    CIR_TensorOf<[I32, F32, I64]>:$start,
+    CIR_TensorOf<[I32, F32, I64]>:$limit,
+    CIR_TensorOf<[I32, F32, I64]>:$delta
+  );
+
+  let results = (outs CIR_TensorOf<[I32, F32, I64]>:$result);
+
+  let hasOptions = 1;
+}
+
 def CIR_ReduceMaxOp: CIR_Op<"reduce_max", [
     PredOpTrait<"input and output must have same element type",
       CIR_TCresVTEtIsSameAsOp<0, 0>>,
diff --git a/circle-mlir/circle-mlir/lib/pass/src/ConvertONNXToCirclePass.cpp b/circle-mlir/circle-mlir/lib/pass/src/ConvertONNXToCirclePass.cpp
index 7fa691f0d11..90a3a505e85 100644
--- a/circle-mlir/circle-mlir/lib/pass/src/ConvertONNXToCirclePass.cpp
+++ b/circle-mlir/circle-mlir/lib/pass/src/ConvertONNXToCirclePass.cpp
@@ -64,6 +64,7 @@
 #include "ops/PowOp.h"
 #include "ops/PReluOp.h"
 #include "ops/QuantizeLinearOp.h"
+#include "ops/RangeOp.h"
 #include "ops/ReciprocalOp.h"
 #include "ops/ReduceMaxOp.h"
 #include "ops/ReduceMeanOp.h"
@@ -247,6 +248,7 @@ void ConvertONNXToCirclePass::runOnOperation()
   patterns.insert<ConvPow>(typeConverter, context);
   patterns.insert<ConvPRelu>(typeConverter, context);
   patterns.insert<ConvQuantizeLinear>(typeConverter, context);
+  patterns.insert<ConvRange>(typeConverter, context);
   patterns.insert<ConvReciprocal>(typeConverter, context);
   patterns.insert<ConvReduceMax>(typeConverter, context);
   patterns.insert<ConvReduceMaxV13>(typeConverter, context);
diff --git a/circle-mlir/circle-mlir/lib/pass/src/ops/RangeOp.h b/circle-mlir/circle-mlir/lib/pass/src/ops/RangeOp.h
new file mode 100644
index 00000000000..dae5f55c697
--- /dev/null
+++ b/circle-mlir/circle-mlir/lib/pass/src/ops/RangeOp.h
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2026 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __CIRCLE_MLIR_PASS_OPS_RANGE_OP_H__
+#define __CIRCLE_MLIR_PASS_OPS_RANGE_OP_H__
+
+#include <circle-mlir/dialect/CircleDialect.h>
+
+#include "ConvertHelper.h"
+
+#include <mlir/Transforms/DialectConversion.h>
+
+#include <src/Dialect/ONNX/ONNXOps.hpp>
+
+namespace mlir
+{
+namespace Circle
+{
+
+class ConvRange : public mlir::OpConversionPattern<mlir::ONNXRangeOp>
+{
+public:
+  using mlir::OpConversionPattern<mlir::ONNXRangeOp>::OpConversionPattern;
+  using OpAdaptor = typename mlir::ONNXRangeOp::Adaptor;
+
+  mlir::LogicalResult matchAndRewrite(mlir::ONNXRangeOp op, OpAdaptor adaptor,
+                                      mlir::ConversionPatternRewriter &rewriter) const override
+  {
+    mlir::Value start = adaptor.getStart();
+    mlir::Value limit = adaptor.getLimit();
+    mlir::Value delta = adaptor.getDelta();
+
+    rewriter.replaceOpWithNewOp<RangeOp>(op, op.getType(), start, limit, delta);
+
+    return mlir::success();
+  }
+};
+
+} // namespace Circle
+} // namespace mlir
+
+#endif // __CIRCLE_MLIR_PASS_OPS_RANGE_OP_H__