diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index afb2f91f65ccd..f3eda90ee0d18 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -45,6 +45,7 @@ Other enhancements
 - :meth:`DataFrame.fillna` and :meth:`Series.fillna` can now accept ``value=None``; for non-object dtype the corresponding NA value will be used (:issue:`57723`)
 - :meth:`DataFrame.pivot_table` and :func:`pivot_table` now allow the passing of keyword arguments to ``aggfunc`` through ``**kwargs`` (:issue:`57884`)
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
+- :meth:`Series.isconstant` checks if a :class:`Series` has constant values, with an optional parameter to drop NaN values (:issue:`58806`)
 - :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
 - Restore support for reading Stata 104-format and enable reading 103-format dta files (:issue:`58554`)
 - Support reading Stata 110-format (Stata 7) dta files (:issue:`47176`)
diff --git a/pandas/core/series.py b/pandas/core/series.py
index a22cc59b62499..25693bad6182e 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -5393,6 +5393,46 @@ def isnull(self) -> Series:
         """
         return super().isnull()
 
+    def isconstant(self, dropna=False):
+        """
+        Return if the series has constant values.
+
+        Parameters
+        ----------
+        dropna : bool, default False
+        If True, NaN values will be ignored. If False, NaN values will be considered
+        in the determination of whether the series has constant values.
+
+        Returns
+        -------
+        bool
+            True if the series has constant values, False otherwise.
+
+        Examples
+        --------
+        >>> s = pd.Series([2, 2, 2, 2])
+        >>> s.isconstant()
+        True
+
+        >>> s = pd.Series([2, 2, 3])
+        >>> s.isconstant()
+        False
+
+        >>> s = pd.Series([2, 2, 2, np.nan])
+        >>> s.isconstant(dropna=True)
+        True
+
+        >>> s = pd.Series([2, 2, 2, np.nan])
+        >>> s.isconstant(dropna=False)
+        False
+        """
+        v = self.to_numpy()
+        if dropna:
+            v = remove_na_arraylike(v)
+        if v.shape[0] == 0 or not notna(v).any():
+            return True
+        return (v[0] == v).all()
+
     # error: Cannot determine type of 'notna'
     @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
     def notna(self) -> Series:
diff --git a/pandas/tests/series/methods/test_isconstant.py b/pandas/tests/series/methods/test_isconstant.py
new file mode 100644
index 0000000000000..e3fd92604802a
--- /dev/null
+++ b/pandas/tests/series/methods/test_isconstant.py
@@ -0,0 +1,65 @@
+import numpy as np
+
+from pandas import Series
+
+
+class TestSeriesIsConstant:
+    def test_isconstant(self):
+        s = Series([2, 2, 2, 2])
+        result = s.isconstant()
+        assert result
+
+        s = Series([2, 2, 2, 3])
+        result = s.isconstant()
+        assert not result
+
+        s = Series([])
+        result = s.isconstant()
+        assert result
+
+        s = Series([5])
+        result = s.isconstant()
+        assert result
+
+    def test_isconstant_with_nan(self):
+        s = Series([np.nan])
+        result = s.isconstant()
+        assert result
+
+        s = Series([np.nan, np.nan])
+        result = s.isconstant()
+        assert result
+
+        s = Series([np.nan, 1])
+        result = s.isconstant()
+        assert not result
+
+        s = Series([np.nan, np.nan, 1])
+        result = s.isconstant()
+        assert not result
+
+    def test_isconstant_with_nan_dropna(self):
+        s = Series([np.nan])
+        result = s.isconstant(True)
+        assert result
+
+        s = Series([np.nan, np.nan])
+        result = s.isconstant(True)
+        assert result
+
+        s = Series([np.nan, 1])
+        result = s.isconstant(True)
+        assert result
+
+        s = Series([np.nan, np.nan, 1])
+        result = s.isconstant(True)
+        assert result
+
+    def test_isconstant_mixed_types(self):
+        s = Series([2, "2", 2])
+        result = s.isconstant()
+        assert not result
+
+        s = Series([2, 2.0, 2])
+        result = s.isconstant()
+        assert result