diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index 610c5f9f1cc33..2375e3e8e4df9 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -1418,7 +1418,24 @@ def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
         if not len(values):
             return np.zeros(len(self), dtype=bool)
 
-        result = pc.is_in(self._pa_array, value_set=pa.array(values))
+        value_set = pa.array(values, from_pandas=True)
+        if pa.types.is_null(value_set.type):
+            # GH#63304: If we have explicit pd.NA, we want to allow the comparison
+            # to return False (not found) rather than raising ArrowInvalid.
+            # However, we need to be careful not to swallow other types that might
+            # be inferred as null (e.g. [np.nan]) which logic elsewhere might rely
+            # on crashing to trigger fallback (e.g. in parsers).
+            has_pd_na = False
+            for x in values:
+                # GH#63304: Check for pd.NA (NAType) specifically
+                if isna(x) and not isinstance(x, (float, np.floating, type(None))):
+                    has_pd_na = True
+                    break
+
+            if has_pd_na:
+                value_set = value_set.cast(self._pa_array.type)
+
+        result = pc.is_in(self._pa_array, value_set=value_set)
         # pyarrow 2.0.0 returned nulls, so we explicitly specify dtype to convert nulls
         # to False
         return np.array(result, dtype=np.bool_)
diff --git a/pandas/tests/indexing/test_gh63304.py b/pandas/tests/indexing/test_gh63304.py
new file mode 100644
index 0000000000000..bba6bd329daad
--- /dev/null
+++ b/pandas/tests/indexing/test_gh63304.py
@@ -0,0 +1,47 @@
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+pa = pytest.importorskip("pyarrow", minversion="13.0.0")
+
+
+def test_drop_na_arrow_index():
+    # GH#63304
+    # Test that dropping pd.NA from PyArrow-backed Index does not raise ArrowInvalid
+
+    # integer
+    df = pd.DataFrame(
+        {"A": [1, 2, 3]}, index=pd.Index([1, 2, 3], dtype="int64[pyarrow]")
+    )
+    # pd.NA is not in index, should raise KeyError, but NOT ArrowInvalid
+    with pytest.raises(KeyError, match="not found in axis"):
+        df.drop(index=[pd.NA])
+
+    # string
+    df = pd.DataFrame(
+        {"A": [1, 2, 3]}, index=pd.Index(["a", "b", "c"], dtype="string[pyarrow]")
+    )
+    with pytest.raises(KeyError, match="not found in axis"):
+        df.drop(index=[pd.NA])
+
+    # binary
+    df = pd.DataFrame(
+        {"A": [1, 2, 3]},
+        index=pd.Index([b"a", b"b", b"c"], dtype="binary[pyarrow]"),
+    )
+    with pytest.raises(KeyError, match="not found in axis"):
+        df.drop(index=[pd.NA])
+
+    # Case where NA IS in the index (should verify it drops correctly)
+    df = pd.DataFrame({"A": [1, 2]}, index=pd.Index([1, pd.NA], dtype="int64[pyarrow]"))
+    result = df.drop(index=[pd.NA])
+    expected = pd.DataFrame({"A": [1]}, index=pd.Index([1], dtype="int64[pyarrow]"))
+    tm.assert_frame_equal(result, expected)
+
+    df = pd.DataFrame(
+        {"A": [1, 2]}, index=pd.Index(["a", pd.NA], dtype="string[pyarrow]")
+    )
+    result = df.drop(index=[pd.NA])
+    expected = pd.DataFrame({"A": [1]}, index=pd.Index(["a"], dtype="string[pyarrow]"))
+    tm.assert_frame_equal(result, expected)