diff --git a/python/pyspark/sql/connect/functions/builtin.py b/python/pyspark/sql/connect/functions/builtin.py
index 7fed175cbc8ea..2a39bc6bfddda 100644
--- a/python/pyspark/sql/connect/functions/builtin.py
+++ b/python/pyspark/sql/connect/functions/builtin.py
@@ -65,7 +65,6 @@
 from pyspark.sql.types import (
     _from_numpy_type,
     DataType,
-    LongType,
     StructType,
     ArrayType,
     StringType,
@@ -2206,12 +2205,9 @@ def schema_of_xml(xml: Union[str, Column], options: Optional[Mapping[str, str]]
 schema_of_xml.__doc__ = pysparkfuncs.schema_of_xml.__doc__
 
 
-def shuffle(col: "ColumnOrName") -> Column:
-    return _invoke_function(
-        "shuffle",
-        _to_col(col),
-        LiteralExpression(random.randint(0, sys.maxsize), LongType()),
-    )
+def shuffle(col: "ColumnOrName", seed: Optional[Union[Column, int]] = None) -> Column:
+    _seed = lit(random.randint(0, sys.maxsize)) if seed is None else lit(seed)
+    return _invoke_function("shuffle", _to_col(col), _seed)
 
 
 shuffle.__doc__ = pysparkfuncs.shuffle.__doc__
diff --git a/python/pyspark/sql/functions/builtin.py b/python/pyspark/sql/functions/builtin.py
index 5f8d1c21a24f1..2d5dbb5946050 100644
--- a/python/pyspark/sql/functions/builtin.py
+++ b/python/pyspark/sql/functions/builtin.py
@@ -17723,7 +17723,7 @@ def array_sort(
 
 
 @_try_remote_functions
-def shuffle(col: "ColumnOrName") -> Column:
+def shuffle(col: "ColumnOrName", seed: Optional[Union[Column, int]] = None) -> Column:
     """
     Array function: Generates a random permutation of the given array.
 
@@ -17736,6 +17736,10 @@ def shuffle(col: "ColumnOrName") -> Column:
     ----------
     col : :class:`~pyspark.sql.Column` or str
         The name of the column or expression to be shuffled.
+    seed : :class:`~pyspark.sql.Column` or int, optional
+        Seed value for the random generator.
+
+        .. versionadded:: 4.0.0
 
     Returns
     -------
@@ -17752,48 +17756,51 @@ def shuffle(col: "ColumnOrName") -> Column:
     Example 1: Shuffling a simple array
 
     >>> import pyspark.sql.functions as sf
-    >>> df = spark.createDataFrame([([1, 20, 3, 5],)], ['data'])
-    >>> df.select(sf.shuffle(df.data)).show() # doctest: +SKIP
-    +-------------+
-    |shuffle(data)|
-    +-------------+
-    |[1, 3, 20, 5]|
-    +-------------+
+    >>> df = spark.sql("SELECT ARRAY(1, 20, 3, 5) AS data")
+    >>> df.select("*", sf.shuffle(df.data, sf.lit(123))).show()
+    +-------------+-------------+
+    |         data|shuffle(data)|
+    +-------------+-------------+
+    |[1, 20, 3, 5]|[5, 1, 20, 3]|
+    +-------------+-------------+
 
     Example 2: Shuffling an array with null values
 
     >>> import pyspark.sql.functions as sf
-    >>> df = spark.createDataFrame([([1, 20, None, 3],)], ['data'])
-    >>> df.select(sf.shuffle(df.data)).show() # doctest: +SKIP
-    +----------------+
-    |   shuffle(data)|
-    +----------------+
-    |[20, 3, NULL, 1]|
-    +----------------+
+    >>> df = spark.sql("SELECT ARRAY(1, 20, NULL, 5) AS data")
+    >>> df.select("*", sf.shuffle(sf.col("data"), 234)).show()
+    +----------------+----------------+
+    |            data|   shuffle(data)|
+    +----------------+----------------+
+    |[1, 20, NULL, 5]|[NULL, 5, 20, 1]|
+    +----------------+----------------+
 
     Example 3: Shuffling an array with duplicate values
 
     >>> import pyspark.sql.functions as sf
-    >>> df = spark.createDataFrame([([1, 2, 2, 3, 3, 3],)], ['data'])
-    >>> df.select(sf.shuffle(df.data)).show() # doctest: +SKIP
-    +------------------+
-    |     shuffle(data)|
-    +------------------+
-    |[3, 2, 1, 3, 2, 3]|
-    +------------------+
+    >>> df = spark.sql("SELECT ARRAY(1, 2, 2, 3, 3, 3) AS data")
+    >>> df.select("*", sf.shuffle("data", 345)).show()
+    +------------------+------------------+
+    |              data|     shuffle(data)|
+    +------------------+------------------+
+    |[1, 2, 2, 3, 3, 3]|[2, 3, 3, 1, 2, 3]|
+    +------------------+------------------+
 
-    Example 4: Shuffling an array with different types of elements
+    Example 4: Shuffling an array with random seed
 
     >>> import pyspark.sql.functions as sf
-    >>> df = spark.createDataFrame([(['a', 'b', 'c', 1, 2, 3],)], ['data'])
-    >>> df.select(sf.shuffle(df.data)).show() # doctest: +SKIP
-    +------------------+
-    |     shuffle(data)|
-    +------------------+
-    |[1, c, 2, a, b, 3]|
-    +------------------+
+    >>> df = spark.sql("SELECT ARRAY(1, 2, 2, 3, 3, 3) AS data")
+    >>> df.select("*", sf.shuffle("data")).show() # doctest: +SKIP
+    +------------------+------------------+
+    |              data|     shuffle(data)|
+    +------------------+------------------+
+    |[1, 2, 2, 3, 3, 3]|[3, 3, 2, 3, 2, 1]|
+    +------------------+------------------+
     """
-    return _invoke_function_over_columns("shuffle", col)
+    if seed is not None:
+        return _invoke_function_over_columns("shuffle", col, lit(seed))
+    else:
+        return _invoke_function_over_columns("shuffle", col)
 
 
 @_try_remote_functions
diff --git a/sql/api/src/main/scala/org/apache/spark/sql/functions.scala b/sql/api/src/main/scala/org/apache/spark/sql/functions.scala
index 0662b8f2b271f..d9bceabe88f8f 100644
--- a/sql/api/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/api/src/main/scala/org/apache/spark/sql/functions.scala
@@ -7252,7 +7252,18 @@ object functions {
    * @group array_funcs
    * @since 2.4.0
    */
-  def shuffle(e: Column): Column = Column.fn("shuffle", e, lit(SparkClassUtils.random.nextLong))
+  def shuffle(e: Column): Column = shuffle(e, lit(SparkClassUtils.random.nextLong))
+
+  /**
+   * Returns a random permutation of the given array.
+   *
+   * @note
+   *   The function is non-deterministic.
+   *
+   * @group array_funcs
+   * @since 4.0.0
+   */
+  def shuffle(e: Column, seed: Column): Column = Column.fn("shuffle", e, seed)
 
   /**
    * Returns a reversed string or an array with reverse order of elements.