diff --git a/lib/Conversion/TorchToStablehlo/GatherScatter.cpp b/lib/Conversion/TorchToStablehlo/GatherScatter.cpp
index 95435dd5805b..314f90dfa807 100644
--- a/lib/Conversion/TorchToStablehlo/GatherScatter.cpp
+++ b/lib/Conversion/TorchToStablehlo/GatherScatter.cpp
@@ -61,12 +61,37 @@ Value gatherTensorAlongSingleAxis(PatternRewriter &rewriter, Operation *op,
                                   Value input, Value indices, int64_t axis,
                                   size_t dimSizeIndexBits) {
   auto loc = op->getLoc();
+
+  auto indicesRankTy = dyn_cast<RankedTensorType>(indices.getType());
+  auto indicesShape = indicesRankTy.getShape();
+  auto inputRankTy = dyn_cast<RankedTensorType>(input.getType());
+
+  // Check if indices tensor is empty (has any dimension with size 0)
+  bool isEmpty =
+      llvm::any_of(indicesShape, [](int64_t dim) { return dim == 0; });
+
+  if (isEmpty) {
+    // Special case: StableHLO doesn't support gather operations on empty
+    // tensors. Return an empty tensor with the correct output shape.
+    auto inputShape = inputRankTy.getShape();
+    SmallVector<int64_t, 4> outputShape(inputShape.begin(),
+                                        inputShape.begin() + axis);
+    outputShape.insert(outputShape.end(), indicesShape.begin(),
+                       indicesShape.end());
+    outputShape.insert(outputShape.end(), inputShape.begin() + axis + 1,
+                       inputShape.end());
+
+    auto outputTy =
+        RankedTensorType::get(outputShape, inputRankTy.getElementType());
+    auto emptyAttr = cast<DenseElementsAttr>(rewriter.getZeroAttr(outputTy));
+    return stablehlo::ConstantOp::create(rewriter, loc, emptyAttr);
+  }
+
   Type intType = rewriter.getIntegerType(dimSizeIndexBits);
   Value one = arith::ConstantOp::create(rewriter, loc,
                                         rewriter.getIntegerAttr(intType, 1));
 
   // sliceSizes
-  auto inputRankTy = dyn_cast<RankedTensorType>(input.getType());
   auto inputRank = inputRankTy.getRank();
   SmallVector<Value, 4> sliceSizes;
   sliceSizes.reserve(inputRank);
@@ -88,7 +113,6 @@ Value gatherTensorAlongSingleAxis(PatternRewriter &rewriter, Operation *op,
   for (int64_t r = 0; r < axis; ++r) {
     offsetDims.push_back(r);
   }
-  auto indicesRankTy = dyn_cast<RankedTensorType>(indices.getType());
   auto indicesRank = indicesRankTy.getRank();
   for (int64_t r = axis + 1; r < inputRank; ++r) {
     offsetDims.push_back(r + indicesRank - 1);
@@ -112,7 +136,6 @@ Value gatherTensorAlongSingleAxis(PatternRewriter &rewriter, Operation *op,
   // outputShape = input.shape[:axis] + indices.shape +
   //                input.shape[axis + 1:]
   auto inputShape = inputRankTy.getShape();
-  auto indicesShape = indicesRankTy.getShape();
   SmallVector<int64_t, 4> outputShape(inputShape.begin(),
                                       inputShape.begin() + axis);
   outputShape.insert(outputShape.end(), indicesShape.begin(),
diff --git a/test/Conversion/TorchToStablehlo/gather.mlir b/test/Conversion/TorchToStablehlo/gather.mlir
index 14581bcc658c..d3d1539f4e08 100644
--- a/test/Conversion/TorchToStablehlo/gather.mlir
+++ b/test/Conversion/TorchToStablehlo/gather.mlir
@@ -63,3 +63,51 @@ func.func @torch.aten.embedding$rank_two_indices(%weight: !torch.vtensor<[?,?],f
   %ret = torch.aten.embedding %weight, %indices, %int-1, %false, %false : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,1], si64>, !torch.int, !torch.bool, !torch.bool -> !torch.vtensor<[?,1,?],f32>
   return %ret: !torch.vtensor<[?,1,?],f32>
 }
+
+
+// -----
+
+// CHECK-LABEL:  func.func @torch.aten.index_select$empty_indices(
+// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[4,8],f32>, %[[ARG1:.*]]: !torch.vtensor<[0],si64>) -> !torch.vtensor<[0,8],f32> {
+// CHECK:         %[[INT0:.*]] = torch.constant.int 0
+// CHECK:         %[[CST:.*]] = stablehlo.constant dense<0.000000e+00> : tensor<0x8xf32>
+// CHECK:         %[[CONVERT:.*]] = stablehlo.convert %[[CST]] : tensor<0x8xf32>
+// CHECK:         %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[CONVERT]] : tensor<0x8xf32> -> !torch.vtensor<[0,8],f32>
+// CHECK:         return %[[RESULT]] : !torch.vtensor<[0,8],f32>
+func.func @torch.aten.index_select$empty_indices(%arg0: !torch.vtensor<[4,8],f32>, %arg1: !torch.vtensor<[0],si64>) -> !torch.vtensor<[0,8],f32> {
+  %int0 = torch.constant.int 0
+  %0 = torch.aten.index_select %arg0, %int0, %arg1 : !torch.vtensor<[4,8],f32>, !torch.int, !torch.vtensor<[0],si64> -> !torch.vtensor<[0,8],f32>
+  return %0 : !torch.vtensor<[0,8],f32>
+}
+
+// -----
+
+// CHECK-LABEL:  func.func @torch.aten.embedding$empty_indices(
+// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[10,8],f32>, %[[ARG1:.*]]: !torch.vtensor<[0],si64>) -> !torch.vtensor<[0,8],f32> {
+// CHECK:         %[[FALSE:.*]] = torch.constant.bool false
+// CHECK:         %[[INT_NEG1:.*]] = torch.constant.int -1
+// CHECK:         %[[CST:.*]] = stablehlo.constant dense<0.000000e+00> : tensor<0x8xf32>
+// CHECK:         %[[CONVERT:.*]] = stablehlo.convert %[[CST]] : tensor<0x8xf32>
+// CHECK:         %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[CONVERT]] : tensor<0x8xf32> -> !torch.vtensor<[0,8],f32>
+// CHECK:         return %[[RESULT]] : !torch.vtensor<[0,8],f32>
+func.func @torch.aten.embedding$empty_indices(%weight: !torch.vtensor<[10,8],f32>, %indices: !torch.vtensor<[0], si64>) -> !torch.vtensor<[0,8],f32> {
+  %false = torch.constant.bool false
+  %int-1 = torch.constant.int -1
+  %ret = torch.aten.embedding %weight, %indices, %int-1, %false, %false : !torch.vtensor<[10,8],f32>, !torch.vtensor<[0], si64>, !torch.int, !torch.bool, !torch.bool -> !torch.vtensor<[0,8],f32>
+  return %ret: !torch.vtensor<[0,8],f32>
+}
+
+// -----
+
+// CHECK-LABEL:  func.func @torch.aten.index_select$empty_indices_dim1(
+// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[4,8],f32>, %[[ARG1:.*]]: !torch.vtensor<[0],si64>) -> !torch.vtensor<[4,0],f32> {
+// CHECK:         %[[INT1:.*]] = torch.constant.int 1
+// CHECK:         %[[CST:.*]] = stablehlo.constant dense<0.000000e+00> : tensor<4x0xf32>
+// CHECK:         %[[CONVERT:.*]] = stablehlo.convert %[[CST]] : tensor<4x0xf32>
+// CHECK:         %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[CONVERT]] : tensor<4x0xf32> -> !torch.vtensor<[4,0],f32>
+// CHECK:         return %[[RESULT]] : !torch.vtensor<[4,0],f32>
+func.func @torch.aten.index_select$empty_indices_dim1(%arg0: !torch.vtensor<[4,8],f32>, %arg1: !torch.vtensor<[0],si64>) -> !torch.vtensor<[4,0],f32> {
+  %int1 = torch.constant.int 1
+  %0 = torch.aten.index_select %arg0, %int1, %arg1 : !torch.vtensor<[4,8],f32>, !torch.int, !torch.vtensor<[0],si64> -> !torch.vtensor<[4,0],f32>
+  return %0 : !torch.vtensor<[4,0],f32>
+}