diff --git a/spark/v4.1/spark/src/main/java/org/apache/iceberg/spark/actions/RemoveDanglingDeletesSparkAction.java b/spark/v4.1/spark/src/main/java/org/apache/iceberg/spark/actions/RemoveDanglingDeletesSparkAction.java
index bbe2847a9216..0ee17d264451 100644
--- a/spark/v4.1/spark/src/main/java/org/apache/iceberg/spark/actions/RemoveDanglingDeletesSparkAction.java
+++ b/spark/v4.1/spark/src/main/java/org/apache/iceberg/spark/actions/RemoveDanglingDeletesSparkAction.java
@@ -113,6 +113,8 @@ Result doExecute() {
    * Dangling delete files can be identified with following steps
    *
    * <ol>
+   *   <li>Load the ENTRIES metadata table once and cache all live entries to avoid scanning
+   *       manifests twice.
    *   <li>Group data files by partition keys and find the minimum data sequence number in each
    *       group.
    *   <li>Left outer join delete files with partition-grouped data files on partition keys.
@@ -123,56 +125,64 @@ Result doExecute() {
    * </ol>
    */
   private List<DeleteFile> findDanglingDeletes() {
-    Dataset<Row> minSequenceNumberByPartition =
-        loadMetadataTable(table, MetadataTableType.ENTRIES)
-            // find live data files
-            .filter("data_file.content == 0 AND status < 2")
-            .selectExpr(
-                "data_file.partition as partition",
-                "data_file.spec_id as spec_id",
-                "sequence_number")
-            .groupBy("partition", "spec_id")
-            .agg(min("sequence_number"))
-            .toDF("grouped_partition", "grouped_spec_id", "min_data_sequence_number");
-
-    Dataset<Row> deleteEntries =
-        loadMetadataTable(table, MetadataTableType.ENTRIES)
-            // find live delete files
-            .filter("data_file.content != 0 AND status < 2");
-
-    Column joinOnPartition =
-        deleteEntries
-            .col("data_file.spec_id")
-            .equalTo(minSequenceNumberByPartition.col("grouped_spec_id"))
-            .and(
-                deleteEntries
-                    .col("data_file.partition")
-                    .equalTo(minSequenceNumberByPartition.col("grouped_partition")));
-
-    Column filterOnDanglingDeletes =
-        col("min_data_sequence_number")
-            // delete files without any data files in partition
-            .isNull()
-            // position delete files without any applicable data files in partition
-            .or(
-                col("data_file.content")
-                    .equalTo("1")
-                    .and(col("sequence_number").$less(col("min_data_sequence_number"))))
-            // equality delete files without any applicable data files in the partition
-            .or(
-                col("data_file.content")
-                    .equalTo("2")
-                    .and(col("sequence_number").$less$eq(col("min_data_sequence_number"))));
-
-    Dataset<Row> danglingDeletes =
-        deleteEntries
-            .join(minSequenceNumberByPartition, joinOnPartition, "left")
-            .filter(filterOnDanglingDeletes)
-            .select("data_file.*");
-    return danglingDeletes.collectAsList().stream()
-        // map on driver because SparkDeleteFile is not serializable
-        .map(row -> deleteFileWrapper(danglingDeletes.schema(), row))
-        .collect(Collectors.toList());
+    // Load the ENTRIES metadata table once and cache to avoid reading all manifests twice
+    Dataset<Row> liveEntries =
+        loadMetadataTable(table, MetadataTableType.ENTRIES).filter("status < 2").cache();
+
+    try {
+      Dataset<Row> minSequenceNumberByPartition =
+          liveEntries
+              // find live data files
+              .filter("data_file.content == 0")
+              .selectExpr(
+                  "data_file.partition as partition",
+                  "data_file.spec_id as spec_id",
+                  "sequence_number")
+              .groupBy("partition", "spec_id")
+              .agg(min("sequence_number"))
+              .toDF("grouped_partition", "grouped_spec_id", "min_data_sequence_number");
+
+      Dataset<Row> deleteEntries =
+          liveEntries
+              // find live delete files
+              .filter("data_file.content != 0");
+
+      Column joinOnPartition =
+          deleteEntries
+              .col("data_file.spec_id")
+              .equalTo(minSequenceNumberByPartition.col("grouped_spec_id"))
+              .and(
+                  deleteEntries
+                      .col("data_file.partition")
+                      .equalTo(minSequenceNumberByPartition.col("grouped_partition")));
+
+      Column filterOnDanglingDeletes =
+          col("min_data_sequence_number")
+              // delete files without any data files in partition
+              .isNull()
+              // position delete files without any applicable data files in partition
+              .or(
+                  col("data_file.content")
+                      .equalTo("1")
+                      .and(col("sequence_number").$less(col("min_data_sequence_number"))))
+              // equality delete files without any applicable data files in the partition
+              .or(
+                  col("data_file.content")
+                      .equalTo("2")
+                      .and(col("sequence_number").$less$eq(col("min_data_sequence_number"))));
+
+      Dataset<Row> danglingDeletes =
+          deleteEntries
+              .join(minSequenceNumberByPartition, joinOnPartition, "left")
+              .filter(filterOnDanglingDeletes)
+              .select("data_file.*");
+      return danglingDeletes.collectAsList().stream()
+          // map on driver because SparkDeleteFile is not serializable
+          .map(row -> deleteFileWrapper(danglingDeletes.schema(), row))
+          .collect(Collectors.toList());
+    } finally {
+      liveEntries.unpersist(false);
+    }
   }
 
   private List<DeleteFile> findDanglingDvs() {