diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/CoalesceShufflePartitions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/CoalesceShufflePartitions.scala
index bd45863652649..d50e32c8b310f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/CoalesceShufflePartitions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/CoalesceShufflePartitions.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.adaptive
 
 import org.apache.spark.sql.SparkSession
 import org.apache.spark.sql.catalyst.plans.physical.SinglePartition
-import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.execution.{ShufflePartitionSpec, SparkPlan}
 import org.apache.spark.sql.execution.exchange.{ENSURE_REQUIREMENTS, REPARTITION, ShuffleExchangeLike, ShuffleOrigin}
 import org.apache.spark.sql.internal.SQLConf
 
@@ -54,8 +54,21 @@ case class CoalesceShufflePartitions(session: SparkSession) extends CustomShuffl
     if (!shuffleStages.forall(s => supportCoalesce(s.shuffle))) {
       plan
     } else {
+      def insertCustomShuffleReader(partitionSpecs: Seq[ShufflePartitionSpec]): SparkPlan = {
+        // This transformation adds new nodes, so we must use `transformUp` here.
+        val stageIds = shuffleStages.map(_.id).toSet
+        plan.transformUp {
+          // even for shuffle exchange whose input RDD has 0 partition, we should still update its
+          // `partitionStartIndices`, so that all the leaf shuffles in a stage have the same
+          // number of output partitions.
+          case stage: ShuffleQueryStageExec if stageIds.contains(stage.id) =>
+            CustomShuffleReaderExec(stage, partitionSpecs)
+        }
+      }
+
       // `ShuffleQueryStageExec#mapStats` returns None when the input RDD has 0 partitions,
       // we should skip it when calculating the `partitionStartIndices`.
+      // If all input RDDs have 0 partition, we create empty partition for every shuffle reader.
       val validMetrics = shuffleStages.flatMap(_.mapStats)
 
       // We may have different pre-shuffle partition numbers, don't reduce shuffle partition number
@@ -63,7 +76,9 @@ case class CoalesceShufflePartitions(session: SparkSession) extends CustomShuffl
       // partition) and a result of a SortMergeJoin (multiple partitions).
       val distinctNumPreShufflePartitions =
         validMetrics.map(stats => stats.bytesByPartitionId.length).distinct
-      if (validMetrics.nonEmpty && distinctNumPreShufflePartitions.length == 1) {
+      if (validMetrics.isEmpty) {
+        insertCustomShuffleReader(ShufflePartitionsUtil.createEmptyPartition() :: Nil)
+      } else if (distinctNumPreShufflePartitions.length == 1) {
         // We fall back to Spark default parallelism if the minimum number of coalesced partitions
         // is not set, so to avoid perf regressions compared to no coalescing.
         val minPartitionNum = conf.getConf(SQLConf.COALESCE_PARTITIONS_MIN_PARTITION_NUM)
@@ -77,15 +92,7 @@ case class CoalesceShufflePartitions(session: SparkSession) extends CustomShuffl
         if (partitionSpecs.length == distinctNumPreShufflePartitions.head) {
           plan
         } else {
-          // This transformation adds new nodes, so we must use `transformUp` here.
-          val stageIds = shuffleStages.map(_.id).toSet
-          plan.transformUp {
-            // even for shuffle exchange whose input RDD has 0 partition, we should still update its
-            // `partitionStartIndices`, so that all the leaf shuffles in a stage have the same
-            // number of output partitions.
-            case stage: ShuffleQueryStageExec if stageIds.contains(stage.id) =>
-              CustomShuffleReaderExec(stage, partitionSpecs)
-          }
+          insertCustomShuffleReader(partitionSpecs)
         }
       } else {
         plan
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ShufflePartitionsUtil.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ShufflePartitionsUtil.scala
index ed92af6adc186..a70a5322a6243 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ShufflePartitionsUtil.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ShufflePartitionsUtil.scala
@@ -125,6 +125,10 @@ object ShufflePartitionsUtil extends Logging {
     partitionSpecs.toSeq
   }
 
+  def createEmptyPartition(): ShufflePartitionSpec = {
+    CoalescedPartitionSpec(0, 0)
+  }
+
   /**
    * Given a list of size, return an array of indices to split the list into multiple partitions,
    * so that the size sum of each partition is close to the target size. Each index indicates the
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
index 31b69211326eb..2598d3ba8be19 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
@@ -1575,4 +1575,19 @@ class AdaptiveQueryExecSuite
       checkNoCoalescePartitions(df.sort($"key"), ENSURE_REQUIREMENTS)
     }
   }
+
+  test("SPARK-35239: Coalesce shuffle partition should handle empty input RDD") {
+    withTable("t") {
+      withSQLConf(SQLConf.COALESCE_PARTITIONS_MIN_PARTITION_NUM.key -> "1",
+        SQLConf.SHUFFLE_PARTITIONS.key -> "2") {
+        spark.sql("CREATE TABLE t (c1 int) USING PARQUET")
+        val (_, adaptive) = runAdaptiveAndVerifyResult("SELECT c1, count(*) FROM t GROUP BY c1")
+        assert(
+          collect(adaptive) {
+            case c @ CustomShuffleReaderExec(_, partitionSpecs) if partitionSpecs.length == 1 => c
+          }.length == 1
+        )
+      }
+    }
+  }
 }