diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index eefe08859c1e9..471a0b0a1f984 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -23,6 +23,9 @@
     iNaT,
     lib,
 )
+
+from pandas._libs.missing import NA
+
 from pandas._typing import (
     AnyArrayLike,
     ArrayLike,
@@ -544,10 +547,20 @@ def isin(comps: ListLike, values: ListLike) -> npt.NDArray[np.bool_]:
     # Ensure np.isin doesn't get object types or it *may* throw an exception
     # Albeit hashmap has O(1) look-up (vs. O(logn) in sorted array),
     # isin is faster for small sizes
+    
+    # GH60678
+    # Ensure values don't contain <NA>, otherwise it throws exception with np.in1d
+    values_contains_NA = False
+    
+    if values.size != 0:  
+        vectorized_check = np.vectorize(lambda v: v is NA)
+        values_contains_NA = vectorized_check(values).any()
+
     if (
         len(comps_array) > _MINIMUM_COMP_ARR_LEN
         and len(values) <= 26
         and comps_array.dtype != object
+        and values_contains_NA == False
     ):
         # If the values include nan we need to check for nan explicitly
         # since np.nan it not equal to np.nan
diff --git a/pandas/tests/series/methods/test_isin.py b/pandas/tests/series/methods/test_isin.py
index e997ae32cf2e2..5b1ff210d134c 100644
--- a/pandas/tests/series/methods/test_isin.py
+++ b/pandas/tests/series/methods/test_isin.py
@@ -211,6 +211,24 @@ def test_isin_large_series_mixed_dtypes_and_nan(monkeypatch):
     tm.assert_series_equal(result, expected)
 
 
+@pytest.mark.parametrize("dtype, data, values, expected", [
+    ("boolean", [pd.NA, False, True], [False, pd.NA], [True, True, False]),
+    ("Int64", [pd.NA, 2, 1], [1, pd.NA], [True, False, True]),
+    ("Float64", [20.0, 30.0, pd.NA], [pd.NA], [False, False, True])
+])
+def test_isin_large_series_and_pdNA(dtype, data, values, expected, monkeypatch):
+    # https://github.com/pandas-dev/pandas/issues/60678
+    # combination of  large series (> _MINIMUM_COMP_ARR_LEN elements) and 
+    # values contains pdNA  
+    min_isin_comp = 2
+    ser = Series(data, dtype=dtype)
+    expected = pd.Series(expected, dtype="boolean")
+
+    with monkeypatch.context() as m:
+        m.setattr(algorithms, "_MINIMUM_COMP_ARR_LEN", min_isin_comp)
+        result = ser.isin(values)
+    tm.assert_series_equal(result, expected)
+
 def test_isin_complex_numbers():
     # GH 17927
     array = [0, 1j, 1j, 1, 1 + 1j, 1 + 2j, 1 + 1j]