diff --git a/doc/source/whatsnew/v1.4.0.rst b/doc/source/whatsnew/v1.4.0.rst
index 5b09b62fa9e88..495322dada350 100644
--- a/doc/source/whatsnew/v1.4.0.rst
+++ b/doc/source/whatsnew/v1.4.0.rst
@@ -159,7 +159,7 @@ Deprecations
 Performance improvements
 ~~~~~~~~~~~~~~~~~~~~~~~~
 - Performance improvement in :meth:`.GroupBy.sample`, especially when ``weights`` argument provided (:issue:`34483`)
--
+- Performance improvement in :meth:`.GroupBy.transform` for user-defined functions (:issue:`41598`)
 
 .. ---------------------------------------------------------------------------
 
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 382cd0e178e15..88d1baae86467 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -1308,12 +1308,15 @@ def _transform_general(self, func, *args, **kwargs):
         gen = self.grouper.get_iterator(obj, axis=self.axis)
         fast_path, slow_path = self._define_paths(func, *args, **kwargs)
 
-        for name, group in gen:
-            if group.size == 0:
-                continue
+        # Determine whether to use slow or fast path by evaluating on the first group.
+        # Need to handle the case of an empty generator and process the result so that
+        # it does not need to be computed again.
+        try:
+            name, group = next(gen)
+        except StopIteration:
+            pass
+        else:
             object.__setattr__(group, "name", name)
-
-            # Try slow path and fast path.
             try:
                 path, res = self._choose_path(fast_path, slow_path, group)
             except TypeError:
@@ -1321,29 +1324,19 @@ def _transform_general(self, func, *args, **kwargs):
             except ValueError as err:
                 msg = "transform must return a scalar value for each group"
                 raise ValueError(msg) from err
-
-            if isinstance(res, Series):
-
-                # we need to broadcast across the
-                # other dimension; this will preserve dtypes
-                # GH14457
-                if res.index.is_(obj.index):
-                    r = concat([res] * len(group.columns), axis=1)
-                    r.columns = group.columns
-                    r.index = group.index
-                else:
-                    r = self.obj._constructor(
-                        np.concatenate([res.values] * len(group.index)).reshape(
-                            group.shape
-                        ),
-                        columns=group.columns,
-                        index=group.index,
-                    )
-
-                applied.append(r)
-            else:
+            if group.size > 0:
+                res = _wrap_transform_general_frame(self.obj, group, res)
                 applied.append(res)
 
+        # Compute and process with the remaining groups
+        for name, group in gen:
+            if group.size == 0:
+                continue
+            object.__setattr__(group, "name", name)
+            res = path(group)
+            res = _wrap_transform_general_frame(self.obj, group, res)
+            applied.append(res)
+
         concat_index = obj.columns if self.axis == 0 else obj.index
         other_axis = 1 if self.axis == 0 else 0  # switches between 0 & 1
         concatenated = concat(applied, axis=self.axis, verify_integrity=False)
@@ -1853,3 +1846,28 @@ def func(df):
         return self._python_apply_general(func, self._obj_with_exclusions)
 
     boxplot = boxplot_frame_groupby
+
+
+def _wrap_transform_general_frame(
+    obj: DataFrame, group: DataFrame, res: DataFrame | Series
+) -> DataFrame:
+    from pandas import concat
+
+    if isinstance(res, Series):
+        # we need to broadcast across the
+        # other dimension; this will preserve dtypes
+        # GH14457
+        if res.index.is_(obj.index):
+            res_frame = concat([res] * len(group.columns), axis=1)
+            res_frame.columns = group.columns
+            res_frame.index = group.index
+        else:
+            res_frame = obj._constructor(
+                np.concatenate([res.values] * len(group.index)).reshape(group.shape),
+                columns=group.columns,
+                index=group.index,
+            )
+        assert isinstance(res_frame, DataFrame)
+        return res_frame
+    else:
+        return res