diff --git a/dask_sql/physical/rel/custom/create_model.py b/dask_sql/physical/rel/custom/create_model.py
index 2e6cdeb0a..1a8f89846 100644
--- a/dask_sql/physical/rel/custom/create_model.py
+++ b/dask_sql/physical/rel/custom/create_model.py
@@ -1,6 +1,7 @@
 import logging
 from typing import TYPE_CHECKING
 
+import numpy as np
 from dask import delayed
 
 from dask_sql.datacontainer import DataContainer
@@ -183,7 +184,16 @@ def convert(self, rel: "LogicalPlan", context: "dask_sql.Context") -> DataContai
 
             delayed_model = [delayed(model.fit)(x_p, y_p) for x_p, y_p in zip(X_d, y_d)]
             model = delayed_model[0].compute()
-            model = ParallelPostFit(estimator=model)
+            if "sklearn" in model_class:
+                output_meta = np.array([])
+                model = ParallelPostFit(
+                    estimator=model,
+                    predict_meta=output_meta,
+                    predict_proba_meta=output_meta,
+                    transform_meta=output_meta,
+                )
+            else:
+                model = ParallelPostFit(estimator=model)
 
         else:
             model.fit(X, y, **fit_kwargs)
diff --git a/dask_sql/physical/rel/custom/predict.py b/dask_sql/physical/rel/custom/predict.py
index eb5e4b69f..dfdf29a25 100644
--- a/dask_sql/physical/rel/custom/predict.py
+++ b/dask_sql/physical/rel/custom/predict.py
@@ -2,6 +2,15 @@
 import uuid
 from typing import TYPE_CHECKING
 
+import numpy as np
+
+try:
+    from dask_ml.wrappers import ParallelPostFit
+
+    dask_ml_flag = True
+except ImportError:  # pragma: no cover
+    dask_ml_flag = False
+
 from dask_sql.datacontainer import ColumnContainer, DataContainer
 from dask_sql.physical.rel.base import BaseRelPlugin
 
@@ -59,7 +68,27 @@ def convert(self, rel: "LogicalPlan", context: "dask_sql.Context") -> DataContai
 
         model, training_columns = context.schema[schema_name].models[model_name]
         df = context.sql(sql_select)
-        prediction = model.predict(df[training_columns])
+        part = df[training_columns]
+
+        if dask_ml_flag:
+            if isinstance(model, ParallelPostFit):
+                output_meta = model.predict_meta
+                if output_meta is None:
+                    output_meta = model.estimator.predict(part._meta_nonempty)
+                try:
+                    prediction = part.map_partitions(
+                        self._predict,
+                        output_meta,
+                        model.estimator,
+                        meta=output_meta,
+                    )
+                except ValueError:
+                    prediction = model.predict(part)
+            else:
+                prediction = model.predict(part)
+        else:
+            prediction = model.predict(part)
+
         predicted_df = df.assign(target=prediction)
 
         # Create a temporary context, which includes the
@@ -79,3 +108,39 @@ def convert(self, rel: "LogicalPlan", context: "dask_sql.Context") -> DataContai
         dc = DataContainer(predicted_df, cc)
 
         return dc
+
+    def _predict(self, part, predict_meta, estimator):
+        if part.shape[0] == 0 and predict_meta is not None:
+            empty_output = self.handle_empty_partitions(predict_meta)
+            if empty_output is not None:
+                return empty_output
+        return estimator.predict(part)
+
+    def handle_empty_partitions(self, output_meta):
+        if hasattr(output_meta, "__array_function__"):
+            if len(output_meta.shape) == 1:
+                shape = 0
+            else:
+                shape = list(output_meta.shape)
+                shape[0] = 0
+            ar = np.zeros(
+                shape=shape,
+                dtype=output_meta.dtype,
+                like=output_meta,
+            )
+            return ar
+        elif "scipy.sparse" in type(output_meta).__module__:
+            # sparse matrices don't support
+            # `like` due to non implimented __array_function__
+            # Refer https://github.com/scipy/scipy/issues/10362
+            # Note below works for both cupy and scipy sparse matrices
+            if len(output_meta.shape) == 1:
+                shape = 0
+            else:
+                shape = list(output_meta.shape)
+                shape[0] = 0
+
+            ar = type(output_meta)(shape, dtype=output_meta.dtype)
+            return ar
+        elif hasattr(output_meta, "iloc"):
+            return output_meta.iloc[:0, :]
diff --git a/tests/integration/test_model.py b/tests/integration/test_model.py
index 044a56fcc..8c97b770c 100644
--- a/tests/integration/test_model.py
+++ b/tests/integration/test_model.py
@@ -924,3 +924,79 @@ def test_experiment_automl_regressor(c, client, training_df):
     ), "Best model was not registered"
 
     check_trained_model(c, "my_automl_exp2")
+
+
+# TODO - many ML tests fail on clusters without sklearn - can we avoid this?
+@skip_if_external_scheduler
+def test_predict_with_nullable_types(c):
+    df = pd.DataFrame(
+        {
+            "rough_day_of_year": [0, 1, 2, 3],
+            "prev_day_inches_rained": [0.0, 1.0, 2.0, 3.0],
+            "rained": [False, False, False, True],
+        }
+    )
+    c.create_table("train_set", df)
+
+    model_class = "'sklearn.linear_model.LogisticRegression'"
+
+    c.sql(
+        f"""
+        CREATE OR REPLACE MODEL model WITH (
+            model_class = {model_class},
+            wrap_predict = True,
+            wrap_fit = False,
+            target_column = 'rained'
+        ) AS (
+            SELECT *
+            FROM train_set
+        )
+        """
+    )
+
+    expected = c.sql(
+        """
+        SELECT * FROM PREDICT(
+            MODEL model,
+            SELECT * FROM train_set
+        )
+        """
+    )
+
+    df = pd.DataFrame(
+        {
+            "rough_day_of_year": pd.Series([0, 1, 2, 3], dtype="Int32"),
+            "prev_day_inches_rained": pd.Series([0.0, 1.0, 2.0, 3.0], dtype="Float32"),
+            "rained": pd.Series([False, False, False, True]),
+        }
+    )
+    c.create_table("train_set", df)
+
+    c.sql(
+        f"""
+        CREATE OR REPLACE MODEL model WITH (
+            model_class = {model_class},
+            wrap_predict = True,
+            wrap_fit = False,
+            target_column = 'rained'
+        ) AS (
+            SELECT *
+            FROM train_set
+        )
+        """
+    )
+
+    result = c.sql(
+        """
+        SELECT * FROM PREDICT(
+            MODEL model,
+            SELECT * FROM train_set
+        )
+        """
+    )
+
+    assert_eq(
+        expected,
+        result,
+        check_dtype=False,
+    )