diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index aa378faac2a00..d70741031cfc2 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -117,6 +117,7 @@ Removal of prior version deprecations/changes
 - Removed ``Series.view`` (:issue:`56054`)
 - Removed ``axis`` argument from :meth:`DataFrame.groupby`, :meth:`Series.groupby`, :meth:`DataFrame.rolling`, :meth:`Series.rolling`, :meth:`DataFrame.resample`, and :meth:`Series.resample` (:issue:`51203`)
 - Removed ``axis`` argument from all groupby operations (:issue:`50405`)
+- Removed ``convert_dtype`` from :meth:`Series.apply` (:issue:`52257`)
 - Removed ``pandas.api.types.is_interval`` and ``pandas.api.types.is_period``, use ``isinstance(obj, pd.Interval)`` and ``isinstance(obj, pd.Period)`` instead (:issue:`55264`)
 - Removed ``pandas.io.sql.execute`` (:issue:`50185`)
 - Removed ``pandas.value_counts``, use :meth:`Series.value_counts` instead (:issue:`53493`)
@@ -129,6 +130,7 @@ Removal of prior version deprecations/changes
 - Removed unused arguments ``*args`` and ``**kwargs`` in :class:`Resampler` methods (:issue:`50977`)
 - Unrecognized timezones when parsing strings to datetimes now raises a ``ValueError`` (:issue:`51477`)
 
+
 .. ---------------------------------------------------------------------------
 .. _whatsnew_300.performance:
 
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index 500f805499a8b..3672cdb13d4a3 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -1635,7 +1635,6 @@ def map_array(
     arr: ArrayLike,
     mapper,
     na_action: Literal["ignore"] | None = None,
-    convert: bool = True,
 ) -> np.ndarray | ExtensionArray | Index:
     """
     Map values using an input mapping or function.
@@ -1647,9 +1646,6 @@ def map_array(
     na_action : {None, 'ignore'}, default None
         If 'ignore', propagate NA values, without passing them to the
         mapping correspondence.
-    convert : bool, default True
-        Try to find better dtype for elementwise function results. If
-        False, leave as dtype=object.
 
     Returns
     -------
@@ -1707,8 +1703,6 @@ def map_array(
     # we must convert to python types
     values = arr.astype(object, copy=False)
     if na_action is None:
-        return lib.map_infer(values, mapper, convert=convert)
+        return lib.map_infer(values, mapper)
     else:
-        return lib.map_infer_mask(
-            values, mapper, mask=isna(values).view(np.uint8), convert=convert
-        )
+        return lib.map_infer_mask(values, mapper, mask=isna(values).view(np.uint8))
diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index 6265faade2281..5218e89a44d18 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -16,7 +16,6 @@
 
 import numpy as np
 
-from pandas._libs import lib
 from pandas._libs.internals import BlockValuesRefs
 from pandas._typing import (
     AggFuncType,
@@ -1376,23 +1375,10 @@ def __init__(
         obj: Series,
         func: AggFuncType,
         *,
-        convert_dtype: bool | lib.NoDefault = lib.no_default,
         by_row: Literal[False, "compat", "_compat"] = "compat",
         args,
         kwargs,
     ) -> None:
-        if convert_dtype is lib.no_default:
-            convert_dtype = True
-        else:
-            warnings.warn(
-                "the convert_dtype parameter is deprecated and will be removed in a "
-                "future version.  Do ``ser.astype(object).apply()`` "
-                "instead if you want ``convert_dtype=False``.",
-                FutureWarning,
-                stacklevel=find_stack_level(),
-            )
-        self.convert_dtype = convert_dtype
-
         super().__init__(
             obj,
             func,
@@ -1486,9 +1472,7 @@ def curried(x):
         # TODO: remove the `na_action="ignore"` when that default has been changed in
         #  Categorical (GH51645).
         action = "ignore" if isinstance(obj.dtype, CategoricalDtype) else None
-        mapped = obj._map_values(
-            mapper=curried, na_action=action, convert=self.convert_dtype
-        )
+        mapped = obj._map_values(mapper=curried, na_action=action)
 
         if len(mapped) and isinstance(mapped[0], ABCSeries):
             # GH#43986 Need to do list(mapped) in order to get treated as nested
diff --git a/pandas/core/base.py b/pandas/core/base.py
index cb8848db9af60..7a3102317396f 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -896,7 +896,7 @@ def hasnans(self) -> bool:
         return bool(isna(self).any())  # type: ignore[union-attr]
 
     @final
-    def _map_values(self, mapper, na_action=None, convert: bool = True):
+    def _map_values(self, mapper, na_action=None):
         """
         An internal function that maps values using the input
         correspondence (which can be a dict, Series, or function).
@@ -908,10 +908,6 @@ def _map_values(self, mapper, na_action=None, convert: bool = True):
         na_action : {None, 'ignore'}
             If 'ignore', propagate NA values, without passing them to the
             mapping function
-        convert : bool, default True
-            Try to find better dtype for elementwise function results. If
-            False, leave as dtype=object. Note that the dtype is always
-            preserved for some extension array dtypes, such as Categorical.
 
         Returns
         -------
@@ -925,7 +921,7 @@ def _map_values(self, mapper, na_action=None, convert: bool = True):
         if isinstance(arr, ExtensionArray):
             return arr.map(mapper, na_action=na_action)
 
-        return algorithms.map_array(arr, mapper, na_action=na_action, convert=convert)
+        return algorithms.map_array(arr, mapper, na_action=na_action)
 
     @final
     def value_counts(
diff --git a/pandas/core/series.py b/pandas/core/series.py
index d5eaa2125b301..92e9ebaefd8ba 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -4481,7 +4481,6 @@ def transform(
     def apply(
         self,
         func: AggFuncType,
-        convert_dtype: bool | lib.NoDefault = lib.no_default,
         args: tuple[Any, ...] = (),
         *,
         by_row: Literal[False, "compat"] = "compat",
@@ -4497,14 +4496,6 @@ def apply(
         ----------
         func : function
             Python function or NumPy ufunc to apply.
-        convert_dtype : bool, default True
-            Try to find better dtype for elementwise function results. If
-            False, leave as dtype=object. Note that the dtype is always
-            preserved for some extension array dtypes, such as Categorical.
-
-            .. deprecated:: 2.1.0
-                ``convert_dtype`` has been deprecated. Do ``ser.astype(object).apply()``
-                instead if you want ``convert_dtype=False``.
         args : tuple
             Positional arguments passed to func after the series value.
         by_row : False or "compat", default "compat"
@@ -4608,7 +4599,6 @@ def apply(
         return SeriesApply(
             self,
             func,
-            convert_dtype=convert_dtype,
             by_row=by_row,
             args=args,
             kwargs=kwargs,
diff --git a/pandas/tests/apply/test_series_apply.py b/pandas/tests/apply/test_series_apply.py
index 1e41e54bff6fc..cd1547620e208 100644
--- a/pandas/tests/apply/test_series_apply.py
+++ b/pandas/tests/apply/test_series_apply.py
@@ -75,17 +75,6 @@ def f(x):
     tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.parametrize("convert_dtype", [True, False])
-def test_apply_convert_dtype_deprecated(convert_dtype):
-    ser = Series(np.random.default_rng(2).standard_normal(10))
-
-    def func(x):
-        return x if x > 0 else np.nan
-
-    with tm.assert_produces_warning(FutureWarning):
-        ser.apply(func, convert_dtype=convert_dtype, by_row="compat")
-
-
 def test_apply_args():
     s = Series(["foo,bar"])