diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index b01866a6d6c82..80ce5b427bf6b 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -302,7 +302,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.api.types.is_float PR01,SA01" \
         -i "pandas.api.types.is_float_dtype SA01" \
         -i "pandas.api.types.is_hashable PR01,RT03,SA01" \
-        -i "pandas.api.types.is_int64_dtype SA01" \
         -i "pandas.api.types.is_integer PR01,SA01" \
         -i "pandas.api.types.is_integer_dtype SA01" \
         -i "pandas.api.types.is_interval_dtype SA01" \
diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py
index bee8af46baa64..fb4daf6bd4ff7 100644
--- a/pandas/core/dtypes/common.py
+++ b/pandas/core/dtypes/common.py
@@ -829,6 +829,13 @@ def is_int64_dtype(arr_or_dtype) -> bool:
     int)` will be True if the OS uses 64-bit integers and False if the OS
     uses 32-bit integers.
 
+    See Also
+    --------
+    pandas.api.types.is_string_dtype : Check whether the provided array or dtype is of the string dtype.
+    pandas.api.types.is_float_dtype : Check whether the provided array or dtype is of a float dtype.
+    pandas.api.types.is_integer_dtype : Check whether the provided array or dtype is of an integer dtype.
+    pandas.api.types.is_numeric_dtype : Check whether the provided array or dtype is of a numeric dtype.
+
     Examples
     --------
     >>> from pandas.api.types import is_int64_dtype