diff --git a/doc/source/reference/arrays.rst b/doc/source/reference/arrays.rst
index 5be08f163e6ce..d37eebef5c0c0 100644
--- a/doc/source/reference/arrays.rst
+++ b/doc/source/reference/arrays.rst
@@ -664,6 +664,7 @@ Data type introspection
     api.types.is_datetime64_dtype
     api.types.is_datetime64_ns_dtype
     api.types.is_datetime64tz_dtype
+    api.types.is_dtype_equal
     api.types.is_extension_array_dtype
     api.types.is_float_dtype
     api.types.is_int64_dtype
diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py
index e92f2363b69f1..68d99937f728c 100644
--- a/pandas/core/dtypes/common.py
+++ b/pandas/core/dtypes/common.py
@@ -655,24 +655,38 @@ def is_dtype_equal(source, target) -> bool:
 
     Parameters
     ----------
-    source : The first dtype to compare
-    target : The second dtype to compare
+    source : type or str
+        The first dtype to compare.
+    target : type or str
+        The second dtype to compare.
 
     Returns
     -------
     boolean
         Whether or not the two dtypes are equal.
 
+    See Also
+    --------
+    api.types.is_categorical_dtype : Check whether the provided array or dtype
+                                            is of the Categorical dtype.
+    api.types.is_string_dtype : Check whether the provided array or dtype
+                                       is of the string dtype.
+    api.types.is_object_dtype : Check whether an array-like or dtype is of the
+                                       object dtype.
+
     Examples
     --------
+    >>> from pandas.api.types import is_dtype_equal
     >>> is_dtype_equal(int, float)
     False
     >>> is_dtype_equal("int", int)
     True
     >>> is_dtype_equal(object, "category")
     False
+    >>> from pandas.core.dtypes.dtypes import CategoricalDtype
     >>> is_dtype_equal(CategoricalDtype(), "category")
     True
+    >>> from pandas.core.dtypes.dtypes import DatetimeTZDtype
     >>> is_dtype_equal(DatetimeTZDtype(tz="UTC"), "datetime64")
     False
     """