diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index ea7850b6f2a5f..cbf409cd24246 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -74,6 +74,7 @@ enhancement2
 Other enhancements
 ^^^^^^^^^^^^^^^^^^
 
+- :meth:`to_sql` with method parameter set to ``multi`` works with Oracle on the backend
 - :attr:`Series.attrs` / :attr:`DataFrame.attrs` now uses a deepcopy for propagating ``attrs`` (:issue:`54134`).
 - :func:`read_csv` now supports ``on_bad_lines`` parameter with ``engine="pyarrow"``. (:issue:`54480`)
 - :func:`read_spss` now returns a :class:`DataFrame` that stores the metadata in :attr:`DataFrame.attrs`. (:issue:`54264`)
diff --git a/pandas/io/sql.py b/pandas/io/sql.py
index 0788d9da06eb9..6a62568fc4ce7 100644
--- a/pandas/io/sql.py
+++ b/pandas/io/sql.py
@@ -963,8 +963,12 @@ def _execute_insert_multi(self, conn, keys: list[str], data_iter) -> int:
         from sqlalchemy import insert
 
         data = [dict(zip(keys, row)) for row in data_iter]
-        stmt = insert(self.table).values(data)
-        result = conn.execute(stmt)
+        stmt = insert(self.table)
+        # conn.execute is used here to ensure compatibility with Oracle.
+        # Using stmt.values(data) would produce a multi row insert that
+        # isn't supported by Oracle.
+        # see: https://docs.sqlalchemy.org/en/20/core/dml.html#sqlalchemy.sql.expression.Insert.values
+        result = conn.execute(stmt, data)
         return result.rowcount
 
     def insert_data(self) -> tuple[list[str], list[np.ndarray]]: