diff --git a/docs/source/release_notes.rst b/docs/source/release_notes.rst
index a9ab610f9e..cde7647c97 100644
--- a/docs/source/release_notes.rst
+++ b/docs/source/release_notes.rst
@@ -12,6 +12,7 @@ Release Notes
     * Fixes
         * Updated ``get_best_sampler_for_data`` to consider all non-numeric datatypes as categorical for SMOTE :pr:`2590`
         * Fixed inconsistent test results from `TargetDistributionDataCheck` :pr:`2608`
+        * Adopted vectorized pd.NA checking for Woodwork 0.5.1 support :pr:`2626`
     * Changes
         * Renamed SMOTE samplers to SMOTE oversampler :pr:`2595`
         * Changed ``partial_dependence`` and ``graph_partial_dependence`` to raise a ``PartialDependenceError`` instead of ``ValueError``. This is not a breaking change because ``PartialDependenceError`` is a subclass of ``ValueError`` :pr:`2604`
diff --git a/evalml/tests/data_checks_tests/test_invalid_targets_data_check.py b/evalml/tests/data_checks_tests/test_invalid_targets_data_check.py
index 6bfe6b4e65..ce5ebfb20c 100644
--- a/evalml/tests/data_checks_tests/test_invalid_targets_data_check.py
+++ b/evalml/tests/data_checks_tests/test_invalid_targets_data_check.py
@@ -54,13 +54,6 @@ def test_invalid_target_data_check_nan_error():
     assert invalid_targets_check.validate(X, y=pd.Series([np.nan, np.nan, np.nan])) == {
         "warnings": [],
         "errors": [
-            DataCheckError(
-                message="Target is unsupported Unknown type. Valid Woodwork "
-                "logical types include: integer, double, boolean",
-                data_check_name=invalid_targets_data_check_name,
-                message_code=DataCheckMessageCode.TARGET_UNSUPPORTED_TYPE,
-                details={"unsupported_type": "unknown"},
-            ).to_dict(),
             DataCheckError(
                 message="Target is either empty or fully null.",
                 data_check_name=invalid_targets_data_check_name,
@@ -794,13 +787,6 @@ def test_invalid_target_data_action_for_all_null(problem_type):
     expected = {
         "warnings": [],
         "errors": [
-            DataCheckError(
-                message="Target is unsupported Unknown type. Valid Woodwork "
-                "logical types include: integer, double, boolean",
-                data_check_name=invalid_targets_data_check_name,
-                message_code=DataCheckMessageCode.TARGET_UNSUPPORTED_TYPE,
-                details={"unsupported_type": "unknown"},
-            ).to_dict(),
             DataCheckError(
                 message="Target is either empty or fully null.",
                 data_check_name=invalid_targets_data_check_name,
diff --git a/evalml/utils/woodwork_utils.py b/evalml/utils/woodwork_utils.py
index 84b2e154dd..2dc37cc908 100644
--- a/evalml/utils/woodwork_utils.py
+++ b/evalml/utils/woodwork_utils.py
@@ -66,7 +66,7 @@ def infer_feature_types(data, feature_types=None):
 
     def convert_all_nan_unknown_to_double(data):
         def is_column_pd_na(data, col):
-            return all([isinstance(x, type(pd.NA)) for x in data[col]])
+            return all(data[col].isna())
 
         def is_column_unknown(data, col):
             return isinstance(data.ww.logical_types[col], Unknown)
@@ -100,7 +100,7 @@ def is_column_unknown(data, col):
         return convert_all_nan_unknown_to_double(data)
 
     if isinstance(data, pd.Series):
-        if all([isinstance(x, type(pd.NA)) for x in data]):
+        if all(data.isna()):
             data = data.replace(pd.NA, np.nan)
             feature_types = "Double"
         return ww.init_series(data, logical_type=feature_types)