diff --git a/mlir/lib/Dialect/Linalg/Transforms/DataLayoutPropagation.cpp b/mlir/lib/Dialect/Linalg/Transforms/DataLayoutPropagation.cpp
--- a/mlir/lib/Dialect/Linalg/Transforms/DataLayoutPropagation.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/DataLayoutPropagation.cpp
@@ -446,6 +446,46 @@
                            *packInfo);
 }
 
+/// Folds pack(fill) into a single fill op if
+///   1. The pack op does not have padding value, or
+///   2. The filled value and padding value are the same.
+static FailureOr<FillOp>
+foldFillPackIntoFillOp(RewriterBase &rewriter, tensor::PackOp packOp,
+                       ControlPropagationFn controlFn) {
+  auto fillOp = packOp.getSource().getDefiningOp<FillOp>();
+  if (!fillOp)
+    return failure();
+
+  // User controlled propagation function.
+  if (!controlFn(fillOp))
+    return failure();
+
+  if (auto paddingValue = packOp.getPaddingValue())
+    if (!isEqualConstantIntOrValue(paddingValue, fillOp.value()))
+      return failure();
+
+  OpBuilder::InsertionGuard guard(rewriter);
+  rewriter.setInsertionPoint(fillOp);
+
+  Value packOpDest = packOp.getDest();
+  if (!packOpDest.hasOneUse())
+    return failure();
+  if (auto emptyOp = packOpDest.getDefiningOp<tensor::EmptyOp>()) {
+    packOpDest = tensor::PackOp::createDestinationTensor(
+        rewriter, fillOp.getLoc(), fillOp.getDpsInitOperand(0)->get(),
+        packOp.getMixedTiles(), packOp.getInnerDimsPos(),
+        packOp.getOuterDimsPerm());
+  } else {
+    DominanceInfo dom(fillOp);
+    if (!dom.properlyDominates(packOpDest, fillOp))
+      return failure();
+  }
+
+  Value fillDest = packOpDest;
+  return clone(rewriter, fillOp, packOpDest.getType(),
+               {fillOp.value(), fillDest});
+}
+
 /// Wrapper pattern that applies bubbleUpPackOpThroughGenericOp method.
 struct BubbleUpPackOpThroughGenericOpPattern
     : public OpRewritePattern<tensor::PackOp> {
@@ -468,6 +508,25 @@
   ControlPropagationFn controlFn;
 };
 
+/// Wrapper pattern that applies foldFillPackIntoFillOp method.
+struct FoldFillPackIntoFillOpPattern : public OpRewritePattern<tensor::PackOp> {
+public:
+  FoldFillPackIntoFillOpPattern(MLIRContext *context, ControlPropagationFn fun)
+      : OpRewritePattern<tensor::PackOp>(context), controlFn(std::move(fun)) {}
+
+  LogicalResult matchAndRewrite(tensor::PackOp packOp,
+                                PatternRewriter &rewriter) const override {
+    auto fillOp = foldFillPackIntoFillOp(rewriter, packOp, controlFn);
+    if (failed(fillOp))
+      return failure();
+    rewriter.replaceOp(packOp, fillOp.value().result());
+    return success();
+  }
+
+private:
+  ControlPropagationFn controlFn;
+};
+
 // TODO: Relax this restriction. We should unpack an elementwise also
 // in the presence of multiple unpack ops as producers.
 /// Return the unpacked operand, if present, for the current generic op.
@@ -689,6 +748,7 @@
     RewritePatternSet &patterns,
     const ControlPropagationFn &controlPackUnPackPropagation) {
   patterns.insert<BubbleUpPackOpThroughGenericOpPattern,
+                  FoldFillPackIntoFillOpPattern,
                   PushDownUnPackOpThroughGenericOp, PushDownUnPackThroughPadOp>(
       patterns.getContext(), controlPackUnPackPropagation);
 }
diff --git a/mlir/test/Dialect/Linalg/data-layout-propagation.mlir b/mlir/test/Dialect/Linalg/data-layout-propagation.mlir
--- a/mlir/test/Dialect/Linalg/data-layout-propagation.mlir
+++ b/mlir/test/Dialect/Linalg/data-layout-propagation.mlir
@@ -837,3 +837,47 @@
 // CHECK-SAME:     inner_dims_pos = [0] inner_tiles = [16]
 // CHECK-SAME:     into %[[UNPACK_NEW_DEST]]
 // CHECK:        return %[[UNPACK]] : tensor<16x540x960xi32>
+
+// -----
+
+func.func @fill_pack() -> tensor<24x32x16x16xf32> {
+  %dest = tensor.empty() : tensor<384x512xf32>
+  %cst = arith.constant 0.000000e+00 : f32
+  %0 = tensor.empty() : tensor<24x32x16x16xf32>
+  %1 = linalg.fill ins(%cst : f32) outs(%dest : tensor<384x512xf32>) -> tensor<384x512xf32>
+  %pack = tensor.pack %1 inner_dims_pos = [0, 1] inner_tiles = [16, 16] into %0 : tensor<384x512xf32> -> tensor<24x32x16x16xf32>
+  return %pack : tensor<24x32x16x16xf32>
+}
+// CHECK-LABEL: func.func @fill_pack
+// CHECK:         %[[PACKED_EMPTY:.+]] = tensor.empty() : tensor<24x32x16x16xf32>
+// CHECK:         %[[FILL:.+]] = linalg.fill ins(%{{.+}}) outs(%[[PACKED_EMPTY]]
+// CHECK:         return %[[FILL]]
+
+// -----
+
+#map = affine_map<()[s0] -> (s0 ceildiv 16)>
+func.func @dynamic_fill_pack(%arg0: tensor<?x?xf32>) -> tensor<?x?x16x16xf32> {
+  %cst = arith.constant 0.000000e+00 : f32
+  %c0 = arith.constant 0 : index
+  %c1 = arith.constant 1 : index
+  %0 = linalg.fill ins(%cst : f32) outs(%arg0 : tensor<?x?xf32>) -> tensor<?x?xf32>
+  %dim = tensor.dim %0, %c0 : tensor<?x?xf32>
+  %dim_0 = tensor.dim %0, %c1 : tensor<?x?xf32>
+  %1 = affine.apply #map()[%dim]
+  %2 = affine.apply #map()[%dim_0]
+  %3 = tensor.empty(%1, %2) : tensor<?x?x16x16xf32>
+  %pack = tensor.pack %0 padding_value(%cst : f32) inner_dims_pos = [0, 1] inner_tiles = [16, 16] into %3 : tensor<?x?xf32> -> tensor<?x?x16x16xf32>
+  return %pack : tensor<?x?x16x16xf32>
+}
+// CHECK-DAG:   #[[MAP:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
+// CHECK:       func.func @dynamic_fill_pack
+// CHECK-SAME:    %[[DEST:[a-zA-Z0-9]+]]
+// CHECK-DAG:     %[[C0:.+]] = arith.constant 0 : index
+// CHECK-DAG:     %[[C1:.+]] = arith.constant 1 : index
+// CHECK:         %[[D0:.+]] = tensor.dim %[[DEST]], %[[C0]]
+// CHECK:         %[[D1:.+]] = tensor.dim %[[DEST]], %[[C1]]
+// CHECK:         %[[PACKED_D0:.+]] = affine.apply #[[MAP]]()[%[[D0]]]
+// CHECK:         %[[PACKED_D1:.+]] = affine.apply #[[MAP]]()[%[[D1]]]
+// CHECK:         %[[PACKED_EMPTY:.+]] = tensor.empty(%[[PACKED_D0]], %[[PACKED_D1]]) : tensor<?x?x16x16xf32>
+// CHECK:         %[[FILL:.+]] = linalg.fill ins(%{{.+}}) outs(%[[PACKED_EMPTY]]
+// CHECK:         return %[[FILL]]