diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index a983bc850057..e041c54c61db 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -244,7 +244,7 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       //   4. Pick cartesian product if join type is inner like.
       //   5. Pick broadcast nested loop join as the final solution. It may OOM but we don't have
       //      other choice.
-      case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right, hint) =>
+      case p @ ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right, hint) =>
         def createBroadcastHashJoin(buildLeft: Boolean, buildRight: Boolean) = {
           val wantToBuildLeft = canBuildLeft(joinType) && buildLeft
           val wantToBuildRight = canBuildRight(joinType) && buildRight
@@ -286,7 +286,7 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
 
         def createCartesianProduct() = {
           if (joinType.isInstanceOf[InnerLike]) {
-            Some(Seq(joins.CartesianProductExec(planLater(left), planLater(right), condition)))
+            Some(Seq(joins.CartesianProductExec(planLater(left), planLater(right), p.condition)))
           } else {
             None
           }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinHintSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinHintSuite.scala
index f68c41694126..71f7a708ad68 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinHintSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinHintSuite.scala
@@ -570,4 +570,31 @@ class JoinHintSuite extends PlanTest with SharedSparkSession with AdaptiveSparkP
       assert(joinHints == expectedHints)
     }
   }
+
+  test("SPARK-32220: Non Cartesian Product Join Result Correct with SHUFFLE_REPLICATE_NL hint") {
+    withTempView("t1", "t2") {
+      Seq((1, "4"), (2, "2")).toDF("key", "value").createTempView("t1")
+      Seq((1, "1"), (2, "12.3"), (2, "123")).toDF("key", "value").createTempView("t2")
+      val df1 = sql("SELECT /*+ shuffle_replicate_nl(t1) */ * from t1 join t2 ON t1.key = t2.key")
+      val df2 = sql("SELECT * from t1 join t2 ON t1.key = t2.key")
+      assert(df1.collect().size == df2.collect().size)
+
+      val df3 = sql("SELECT /*+ shuffle_replicate_nl(t1) */ * from t1 join t2")
+      val df4 = sql("SELECT * from t1 join t2")
+      assert(df3.collect().size == df4.collect().size)
+
+      val df5 = sql("SELECT /*+ shuffle_replicate_nl(t1) */ * from t1 join t2 ON t1.key < t2.key")
+      val df6 = sql("SELECT * from t1 join t2 ON t1.key < t2.key")
+      assert(df5.collect().size == df6.collect().size)
+
+      val df7 = sql("SELECT /*+ shuffle_replicate_nl(t1) */ * from t1 join t2 ON t1.key < 2")
+      val df8 = sql("SELECT * from t1 join t2 ON t1.key < 2")
+      assert(df7.collect().size == df8.collect().size)
+
+
+      val df9 = sql("SELECT /*+ shuffle_replicate_nl(t1) */ * from t1 join t2 ON t2.key < 2")
+      val df10 = sql("SELECT * from t1 join t2 ON t2.key < 2")
+      assert(df9.collect().size == df10.collect().size)
+    }
+  }
 }