diff --git a/mlir/lib/Dialect/Vector/IR/VectorOps.cpp b/mlir/lib/Dialect/Vector/IR/VectorOps.cpp
--- a/mlir/lib/Dialect/Vector/IR/VectorOps.cpp
+++ b/mlir/lib/Dialect/Vector/IR/VectorOps.cpp
@@ -5136,12 +5136,81 @@
   }
 };
 
+static VectorType trimTrailingOneDims(VectorType oldType) {
+  ArrayRef<int64_t> oldShape = oldType.getShape();
+  ArrayRef<int64_t> newShape(oldShape.begin(), oldShape.end());
+
+  while (newShape.back() == 1)
+    newShape = newShape.drop_back(1);
+
+  auto newScalableDims =
+      oldType.getScalableDims().drop_front(oldShape.size() - newShape.size());
+
+  // Make sure we have at least 1 dimension per vector type requirements.
+  if (newShape.empty()) {
+    newShape = oldShape.take_back();
+    newScalableDims = oldType.getScalableDims().take_back();
+  }
+  return VectorType::get(newShape, oldType.getElementType(), newScalableDims);
+}
+
+/// Folds shape_cast(create_mask) into a new create_mask
+class FoldShapeCastCreateMask final : public OpRewritePattern<ShapeCastOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(ShapeCastOp shapeOp,
+                                PatternRewriter &rewriter) const override {
+    Value shapeOpSrc = shapeOp->getOperand(0);
+    auto createMaskOp = shapeOpSrc.getDefiningOp<vector::CreateMaskOp>();
+    auto constantMaskOp = shapeOpSrc.getDefiningOp<vector::ConstantMaskOp>();
+    if (!createMaskOp && !constantMaskOp)
+      return failure();
+
+    VectorType shapeOpResTy = shapeOp.getResultVectorType();
+    VectorType shapeOpSrcTy = shapeOp.getSourceVectorType();
+
+    VectorType newVecType = trimTrailingOneDims(shapeOpSrcTy);
+    if (newVecType != shapeOpResTy)
+      return failure();
+
+    if (createMaskOp) {
+      auto maskOperands = createMaskOp.getOperands();
+      auto numDimsToDrop =
+          shapeOpSrcTy.getShape().size() - shapeOpResTy.getShape().size();
+      auto numMaskOperands = maskOperands.size();
+
+      // Check every mask operand whether it can be dropped
+      for (size_t i = numMaskOperands - 1; i >= numMaskOperands - numDimsToDrop;
+           i--) {
+        auto constant = maskOperands[i].getDefiningOp<arith::ConstantIndexOp>();
+        // TODO: Support mask values other than 1 (0 might be only viable
+        // option)
+        if (!constant || (constant.value() != 1))
+          return failure();
+      }
+      SmallVector<Value> newMaskOperands =
+          maskOperands.drop_back(numDimsToDrop);
+
+      rewriter.replaceOpWithNewOp<vector::CreateMaskOp>(
+          shapeOp, shapeOp.getResultVectorType(), newMaskOperands);
+      return success();
+    }
+
+    // TODO: Constant mask case
+
+    return failure();
+  }
+};
+
 } // namespace
 
 void vector::TransposeOp::getCanonicalizationPatterns(
     RewritePatternSet &results, MLIRContext *context) {
-  results.add<FoldTransposeCreateMask, FoldTransposedScalarBroadcast,
-              TransposeFolder, FoldTransposeSplat>(context);
+  results
+      .add<FoldShapeCastCreateMask, FoldTransposeCreateMask,
+           FoldTransposedScalarBroadcast, TransposeFolder, FoldTransposeSplat>(
+          context);
 }
 
 void vector::TransposeOp::getTransp(SmallVectorImpl<int64_t> &results) {
diff --git a/mlir/test/Dialect/Vector/canonicalize.mlir b/mlir/test/Dialect/Vector/canonicalize.mlir
--- a/mlir/test/Dialect/Vector/canonicalize.mlir
+++ b/mlir/test/Dialect/Vector/canonicalize.mlir
@@ -2196,3 +2196,21 @@
   %0 = vector.mask %all_true { arith.addf %a, %a : vector<3x4xf32> } : vector<3x4xi1> -> vector<3x4xf32>
   return %0 : vector<3x4xf32>
 }
+
+// -----
+
+// CHECK-LABEL:   func.func @fold_shape_cast_with_mask(
+// CHECK-SAME:     %[[VAL_0:.*]]: tensor<1x?xf32>) -> vector<1x4xi1> {
+func.func @fold_shape_cast_with_mask(%arg0: tensor<1x?xf32>) -> vector<1x4xi1> {
+// CHECK-NOT: vector.shape_cast
+// CHECK:     %[[VAL_1:.*]] = arith.constant 1 : index
+// CHECK:     %[[VAL_2:.*]] = tensor.dim %[[VAL_0]], %[[VAL_1]] : tensor<1x?xf32>
+// CHECK:     %[[VAL_3:.*]] = vector.create_mask %[[VAL_1]], %[[VAL_2]] : vector<1x4xi1>
+// CHECK:     return %[[VAL_3]] : vector<1x4xi1>
+  %c1 = arith.constant 1 : index
+  %c0 = arith.constant 0 : index
+  %dim_0 = tensor.dim %arg0, %c1 : tensor<1x?xf32>
+  %1 = vector.create_mask %c1, %dim_0, %c1, %c1 : vector<1x4x1x1xi1>
+  %2 = vector.shape_cast %1 : vector<1x4x1x1xi1> to vector<1x4xi1>
+  return %2 : vector<1x4xi1>
+}