diff --git a/doc/source/whatsnew/v1.2.3.rst b/doc/source/whatsnew/v1.2.3.rst
index f72ee78bf243a..99e997189d7b8 100644
--- a/doc/source/whatsnew/v1.2.3.rst
+++ b/doc/source/whatsnew/v1.2.3.rst
@@ -24,6 +24,8 @@ Fixed regressions
   Passing ``ascending=None`` is still considered invalid,
   and the new error message suggests a proper usage
   (``ascending`` must be a boolean or a list-like boolean).
+- Fixed regression in :meth:`DataFrame.transform` and :meth:`Series.transform` giving incorrect column labels when passed a dictionary with a mix of list and non-list values (:issue:`40018`)
+-
 
 .. ---------------------------------------------------------------------------
 
diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index db4203e5158ef..970629f4abfe9 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -280,7 +280,7 @@ def transform_dict_like(self, func):
         if len(func) == 0:
             raise ValueError("No transform functions were provided")
 
-        self.validate_dictlike_arg("transform", obj, func)
+        func = self.normalize_dictlike_arg("transform", obj, func)
 
         results: Dict[Hashable, FrameOrSeriesUnion] = {}
         for name, how in func.items():
@@ -421,32 +421,17 @@ def agg_dict_like(self, _axis: int) -> FrameOrSeriesUnion:
         -------
         Result of aggregation.
         """
+        from pandas.core.reshape.concat import concat
+
         obj = self.obj
         arg = cast(AggFuncTypeDict, self.f)
 
-        is_aggregator = lambda x: isinstance(x, (list, tuple, dict))
-
         if _axis != 0:  # pragma: no cover
             raise ValueError("Can only pass dict with axis=0")
 
         selected_obj = obj._selected_obj
 
-        self.validate_dictlike_arg("agg", selected_obj, arg)
-
-        # if we have a dict of any non-scalars
-        # eg. {'A' : ['mean']}, normalize all to
-        # be list-likes
-        # Cannot use arg.values() because arg may be a Series
-        if any(is_aggregator(x) for _, x in arg.items()):
-            new_arg: AggFuncTypeDict = {}
-            for k, v in arg.items():
-                if not isinstance(v, (tuple, list, dict)):
-                    new_arg[k] = [v]
-                else:
-                    new_arg[k] = v
-            arg = new_arg
-
-        from pandas.core.reshape.concat import concat
+        arg = self.normalize_dictlike_arg("agg", selected_obj, arg)
 
         if selected_obj.ndim == 1:
             # key only used for output
@@ -540,14 +525,15 @@ def maybe_apply_multiple(self) -> Optional[FrameOrSeriesUnion]:
             return None
         return self.obj.aggregate(self.f, self.axis, *self.args, **self.kwargs)
 
-    def validate_dictlike_arg(
+    def normalize_dictlike_arg(
         self, how: str, obj: FrameOrSeriesUnion, func: AggFuncTypeDict
-    ) -> None:
+    ) -> AggFuncTypeDict:
         """
-        Raise if dict-like argument is invalid.
+        Handler for dict-like argument.
 
         Ensures that necessary columns exist if obj is a DataFrame, and
-        that a nested renamer is not passed.
+        that a nested renamer is not passed. Also normalizes to all lists
+        when values consists of a mix of list and non-lists.
         """
         assert how in ("apply", "agg", "transform")
 
@@ -567,6 +553,23 @@ def validate_dictlike_arg(
                 cols_sorted = list(safe_sort(list(cols)))
                 raise KeyError(f"Column(s) {cols_sorted} do not exist")
 
+        is_aggregator = lambda x: isinstance(x, (list, tuple, dict))
+
+        # if we have a dict of any non-scalars
+        # eg. {'A' : ['mean']}, normalize all to
+        # be list-likes
+        # Cannot use func.values() because arg may be a Series
+        if any(is_aggregator(x) for _, x in func.items()):
+            new_func: AggFuncTypeDict = {}
+            for k, v in func.items():
+                if not is_aggregator(v):
+                    # mypy can't realize v is not a list here
+                    new_func[k] = [v]  # type:ignore[list-item]
+                else:
+                    new_func[k] = v
+            func = new_func
+        return func
+
 
 class FrameApply(Apply):
     obj: DataFrame
diff --git a/pandas/tests/apply/test_frame_transform.py b/pandas/tests/apply/test_frame_transform.py
index 1888ddd8ec4aa..47bc69656a597 100644
--- a/pandas/tests/apply/test_frame_transform.py
+++ b/pandas/tests/apply/test_frame_transform.py
@@ -103,6 +103,17 @@ def test_transform_dictlike(axis, float_frame, box):
     tm.assert_frame_equal(result, expected)
 
 
+def test_transform_dictlike_mixed():
+    # GH 40018 - mix of lists and non-lists in values of a dictionary
+    df = DataFrame({"a": [1, 2], "b": [1, 4], "c": [1, 4]})
+    result = df.transform({"b": ["sqrt", "abs"], "c": "sqrt"})
+    expected = DataFrame(
+        [[1.0, 1, 1.0], [2.0, 4, 2.0]],
+        columns=MultiIndex([("b", "c"), ("sqrt", "abs")], [(0, 0, 1), (0, 1, 0)]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
 @pytest.mark.parametrize(
     "ops",
     [
diff --git a/pandas/tests/apply/test_series_transform.py b/pandas/tests/apply/test_series_transform.py
index e67ea4f14e4ac..24d619cb2bbb1 100644
--- a/pandas/tests/apply/test_series_transform.py
+++ b/pandas/tests/apply/test_series_transform.py
@@ -2,6 +2,8 @@
 import pytest
 
 from pandas import (
+    DataFrame,
+    MultiIndex,
     Series,
     concat,
 )
@@ -55,6 +57,17 @@ def test_transform_dictlike(string_series, box):
     tm.assert_frame_equal(result, expected)
 
 
+def test_transform_dictlike_mixed():
+    # GH 40018 - mix of lists and non-lists in values of a dictionary
+    df = Series([1, 4])
+    result = df.transform({"b": ["sqrt", "abs"], "c": "sqrt"})
+    expected = DataFrame(
+        [[1.0, 1, 1.0], [2.0, 4, 2.0]],
+        columns=MultiIndex([("b", "c"), ("sqrt", "abs")], [(0, 0, 1), (0, 1, 0)]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
 def test_transform_wont_agg(string_series):
     # GH 35964
     # we are trying to transform with an aggregator