diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index 0477815f329bf..a7acdd2c018ea 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1093,6 +1093,11 @@ getTensorExtractMemoryAccessPattern(tensor::ExtractOp extractOp,
   if (inputShape.getShape().empty())
     return VectorMemoryAccessKind::ScalarBroadcast;
 
+  // 0a. Is the result a 0-D vector? If yes, there are no iteration dimensions
+  // so the tensor.extract is a single scalar load regardless of the index.
+  if (resType.getRank() == 0)
+    return VectorMemoryAccessKind::ScalarBroadcast;
+
   // True for vectors that are effectively 1D, e.g. `vector<1x4x1xi32>`, false
   // otherwise.
   bool isOutput1DVector =
@@ -1254,19 +1259,22 @@ vectorizeTensorExtract(RewriterBase &rewriter, VectorizationState &state,
         rewriter, loc, resultType, extractOp.getTensor(), transferReadIdxs,
         /*padding=*/std::nullopt, permutationMap, inBounds);
 
-    // Mask this broadcasting xfer_read here rather than relying on the generic
-    // path (the generic path assumes identity masking map, which wouldn't be
-    // valid here).
-    SmallVector<int64_t> readMaskShape = {1};
-    auto readMaskType = VectorType::get(readMaskShape, rewriter.getI1Type());
-    auto allTrue = vector::ConstantMaskOp::create(
-        rewriter, loc, readMaskType, vector::ConstantMaskKind::AllTrue);
-    auto *maskedReadOp =
-        mlir::vector::maskOperation(rewriter, transferReadOp, allTrue);
+    Operation *readOrMaskedReadOp = transferReadOp;
+    if (dstRank > 0) {
+      // Mask this broadcasting xfer_read here rather than relying on the
+      // generic path (the generic path assumes identity masking map, which
+      // wouldn't be valid here).
+      SmallVector<int64_t> readMaskShape = {1};
+      auto readMaskType = VectorType::get(readMaskShape, rewriter.getI1Type());
+      auto allTrue = vector::ConstantMaskOp::create(
+          rewriter, loc, readMaskType, vector::ConstantMaskKind::AllTrue);
+      readOrMaskedReadOp =
+          mlir::vector::maskOperation(rewriter, transferReadOp, allTrue);
+    }
 
     LDBG() << "Vectorised as scalar broadcast load: " << extractOp;
     return VectorizationHookResult{VectorizationHookStatus::NewOp,
-                                   maskedReadOp};
+                                   readOrMaskedReadOp};
   }
 
   // 2b. Handle contiguous access.
diff --git a/mlir/test/Dialect/Linalg/vectorization/extract-with-patterns.mlir b/mlir/test/Dialect/Linalg/vectorization/extract-with-patterns.mlir
index d7722eac2b91f..e04a3f1a83d35 100644
--- a/mlir/test/Dialect/Linalg/vectorization/extract-with-patterns.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization/extract-with-patterns.mlir
@@ -684,3 +684,39 @@ func.func @vectorize_nd_tensor_extract_transfer_read_basic_column(
 // CHECK:           %[[READ:.*]] = vector.transfer_read %[[SRC]][%[[C0]], %[[C0]], %[[C0]]], %[[PV]] : tensor<3x3x3xf32>, vector<f32>
 // CHECK:           %[[READ_BCAST:.*]] = vector.broadcast %[[READ]] : vector<f32> to vector<3x1x1xf32>
 // CHECK:           vector.transfer_write %[[READ_BCAST]], %[[INIT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<3x1x1xf32>, tensor<3x1x1xf32>
+
+// -----
+
+// Rank-0 linalg.generic with tensor.extract using a data-dependent index.
+// The tensor.extract should be classified as ScalarBroadcast (not Gather),
+// producing a vector.transfer_read of a 0-D vector.
+
+func.func @rank0_tensor_extract_data_dependent_index(
+    %src: tensor<2xi64>,
+    %idx_tensor: tensor<i64>) -> tensor<i64> {
+
+  %init = tensor.empty() : tensor<i64>
+  %res = linalg.generic {
+    indexing_maps = [affine_map<() -> ()>, affine_map<() -> ()>],
+    iterator_types = []}
+    ins(%idx_tensor : tensor<i64>) outs(%init : tensor<i64>) {
+  ^bb0(%in: i64, %out: i64):
+    %idx = arith.index_cast %in : i64 to index
+    %val = tensor.extract %src[%idx] : tensor<2xi64>
+    linalg.yield %val : i64
+  } -> tensor<i64>
+
+  return %res : tensor<i64>
+}
+
+// CHECK-LABEL: func.func @rank0_tensor_extract_data_dependent_index(
+// CHECK-SAME:      %[[SRC:.*]]: tensor<2xi64>,
+// CHECK-SAME:      %[[IDX_TENSOR:.*]]: tensor<i64>) -> tensor<i64> {
+// CHECK-DAG:       %[[INIT:.*]] = tensor.empty() : tensor<i64>
+// CHECK-DAG:       %[[PAD:.*]] = ub.poison : i64
+// CHECK:           %[[READ_IDX:.*]] = vector.transfer_read %[[IDX_TENSOR]][], %[[PAD]] : tensor<i64>, vector<i64>
+// CHECK:           %[[SCALAR_IDX:.*]] = vector.extract %[[READ_IDX]][] : i64 from vector<i64>
+// CHECK:           %[[INDEX:.*]] = arith.index_cast %[[SCALAR_IDX]] : i64 to index
+// CHECK:           %[[READ_VAL:.*]] = vector.transfer_read %[[SRC]][%[[INDEX]]], %{{.*}} : tensor<2xi64>, vector<i64>
+// CHECK:           %[[WRITE:.*]] = vector.transfer_write %[[READ_VAL]], %[[INIT]][] : vector<i64>, tensor<i64>
+// CHECK:           return %[[WRITE]] : tensor<i64>