diff --git a/CHANGELOG.md b/CHANGELOG.md
index 709cced4b680..50956f5b9432 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,6 +15,8 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
 
 ### Fixed
 
+- Fixed `to_dense_adj` function by ordering the node idices by batch ([#10535](https://github.com/pyg-team/pytorch_geometric/pull/10535))
+
 ### Security
 
 ## [2.7.0] - 2025-10-14
diff --git a/test/utils/test_to_dense_adj.py b/test/utils/test_to_dense_adj.py
index a6c8a94981f3..77a298150e5a 100644
--- a/test/utils/test_to_dense_adj.py
+++ b/test/utils/test_to_dense_adj.py
@@ -106,3 +106,28 @@ def test_to_dense_adj_with_duplicate_entries():
         [0.0, 0.0, 13.0],
         [8.0, 0.0, 0.0],
     ]
+
+
+def test_to_dense_adj_with_unordered_batch():
+    edge_index = torch.tensor([
+        [0, 1, 2, 3],
+        [3, 2, 1, 0],
+    ])
+    batch = torch.tensor([0, 1, 1, 0])
+
+    adj = to_dense_adj(edge_index, batch)
+    assert adj.size() == (2, 2, 2)
+    assert adj[0].tolist() == [[0.0, 1.0], [1.0, 0.0]]
+    assert adj[1].tolist() == [[0.0, 1.0], [1.0, 0.0]]
+
+    edge_index = torch.tensor([
+        [0, 1, 2, 3],
+        [3, 2, 1, 0],
+    ])
+    batch = torch.tensor([0, 1, 1, 0])
+    edge_attr = torch.tensor([1.0, 3.0, 4.0, 2.0])
+
+    adj = to_dense_adj(edge_index, batch, edge_attr)
+    assert adj.size() == (2, 2, 2)
+    assert adj[0].tolist() == [[0.0, 1.0], [2.0, 0.0]]
+    assert adj[1].tolist() == [[0.0, 3.0], [4.0, 0.0]]
diff --git a/torch_geometric/utils/_to_dense_adj.py b/torch_geometric/utils/_to_dense_adj.py
index 5ca5758956e5..0ac174e69e72 100644
--- a/torch_geometric/utils/_to_dense_adj.py
+++ b/torch_geometric/utils/_to_dense_adj.py
@@ -67,6 +67,12 @@ def to_dense_adj(
     if batch_size is None:
         batch_size = int(batch.max()) + 1 if batch.numel() > 0 else 1
 
+    perm = batch.argsort()
+    batch = batch[perm]
+    new_index_map = torch.empty_like(perm)
+    new_index_map[perm] = torch.arange(perm.size(0))
+    edge_index = new_index_map[edge_index]
+
     one = batch.new_ones(batch.size(0))
     num_nodes = scatter(one, batch, dim=0, dim_size=batch_size, reduce='sum')
     cum_nodes = cumsum(num_nodes)