diff --git a/sql-plugin/src/main/scala/org/apache/spark/sql/rapids/execution/GpuBroadcastNestedLoopJoinExecBase.scala b/sql-plugin/src/main/scala/org/apache/spark/sql/rapids/execution/GpuBroadcastNestedLoopJoinExecBase.scala
index 7ddc969d919..578c1106eb1 100644
--- a/sql-plugin/src/main/scala/org/apache/spark/sql/rapids/execution/GpuBroadcastNestedLoopJoinExecBase.scala
+++ b/sql-plugin/src/main/scala/org/apache/spark/sql/rapids/execution/GpuBroadcastNestedLoopJoinExecBase.scala
@@ -21,6 +21,7 @@ import ai.rapids.cudf.{ast, GatherMap, NvtxColor, OutOfBoundsPolicy, Scalar, Tab
 import ai.rapids.cudf.ast.CompiledExpression
 import com.nvidia.spark.rapids._
 import com.nvidia.spark.rapids.Arm.{closeOnExcept, withResource}
+import com.nvidia.spark.rapids.RapidsPluginImplicits.AutoCloseableProducingArray
 import com.nvidia.spark.rapids.RmmRapidsRetryIterator.{withRestoreOnRetry, withRetry, withRetryNoSplit}
 import com.nvidia.spark.rapids.shims.{GpuBroadcastJoinMeta, ShimBinaryExecNode}
 
@@ -35,7 +36,7 @@ import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.sql.execution.adaptive.BroadcastQueryStageExec
 import org.apache.spark.sql.execution.exchange.ReusedExchangeExec
 import org.apache.spark.sql.execution.joins.BroadcastNestedLoopJoinExec
-import org.apache.spark.sql.types.BooleanType
+import org.apache.spark.sql.types.{BooleanType, DataType}
 import org.apache.spark.sql.vectorized.{ColumnarBatch, ColumnVector}
 
 abstract class GpuBroadcastNestedLoopJoinMetaBase(
@@ -562,7 +563,7 @@ abstract class GpuBroadcastNestedLoopJoinExecBase(
     condition.forall {
       case GpuLiteral(true, BooleanType) =>
         // Spark can generate a degenerate conditional join when the join keys are constants
-        output.isEmpty
+        true
       case GpuAlias(e: GpuExpression, _) => isUnconditionalJoin(Some(e))
       case _ => false
     }
@@ -582,23 +583,12 @@ abstract class GpuBroadcastNestedLoopJoinExecBase(
 
     val broadcastRelation = getBroadcastRelation()
 
-    val joinCondition = boundCondition.orElse {
-      // For outer joins use a true condition if there are any columns in the build side
-      // otherwise use a cross join.
-      val useTrueCondition = joinType match {
-        case LeftOuter if gpuBuildSide == GpuBuildRight => right.output.nonEmpty
-        case RightOuter if gpuBuildSide == GpuBuildLeft => left.output.nonEmpty
-        case _ => false
-      }
-      if (useTrueCondition) Some(GpuLiteral(true)) else None
-    }
-
     // Sometimes Spark specifies a true condition for a row-count-only join.
     // This can happen when the join keys are detected to be constant.
-    if (isUnconditionalJoin(joinCondition)) {
+    if (isUnconditionalJoin(boundCondition)) {
       doUnconditionalJoin(broadcastRelation)
     } else {
-      doConditionalJoin(broadcastRelation, joinCondition, numFirstTableColumns)
+      doConditionalJoin(broadcastRelation, boundCondition, numFirstTableColumns)
     }
   }
 
@@ -627,6 +617,7 @@ abstract class GpuBroadcastNestedLoopJoinExecBase(
       val buildTime = gpuLongMetric(BUILD_TIME)
       val opTime = gpuLongMetric(OP_TIME)
       val buildDataSize = gpuLongMetric(BUILD_DATA_SIZE)
+      val localJoinType = joinType
       // NOTE: this is a def because we want a brand new `ColumnarBatch` to be returned
       // per partition (task), since each task is going to be taking ownership
       // of a columnar batch via `LazySpillableColumnarBatch`.
@@ -664,13 +655,23 @@ abstract class GpuBroadcastNestedLoopJoinExecBase(
             val spillableBuiltBatch = withResource(builtBatch) {
               LazySpillableColumnarBatch(_, "built_batch")
             }
-            new CrossJoinIterator(
-              spillableBuiltBatch,
-              lazyStream,
-              targetSizeBytes,
-              buildSide,
-              opTime = opTime,
-              joinTime = joinTime)
+
+            localJoinType match {
+              case LeftOuter if spillableBuiltBatch.numRows == 0 =>
+                new EmptyOuterNestedLoopJoinIterator(streamedIter, spillableBuiltBatch.dataTypes,
+                  true)
+              case RightOuter if spillableBuiltBatch.numRows == 0 =>
+                new EmptyOuterNestedLoopJoinIterator(streamedIter, spillableBuiltBatch.dataTypes,
+                  false)
+              case _ =>
+                new CrossJoinIterator(
+                  spillableBuiltBatch,
+                  lazyStream,
+                  targetSizeBytes,
+                  buildSide,
+                  opTime = opTime,
+                  joinTime = joinTime)
+            }
           }
       }
       joinIterator.map { cb =>
@@ -825,3 +826,30 @@ class ConditionalNestedLoopExistenceJoinIterator(
     }
   }
 }
+
+/** Iterator for producing batches from an outer join where the build-side table is empty. */
+class EmptyOuterNestedLoopJoinIterator(
+    streamIter: Iterator[ColumnarBatch],
+    buildTypes: Array[DataType],
+    isStreamFirst: Boolean) extends Iterator[ColumnarBatch] {
+  override def hasNext: Boolean = streamIter.hasNext
+
+  override def next(): ColumnarBatch = {
+    withResource(streamIter.next()) { streamBatch =>
+      withResource(buildNullBatch(streamBatch.numRows())) { nullBatch =>
+        if (isStreamFirst) {
+          GpuColumnVector.combineColumns(streamBatch, nullBatch)
+        } else {
+          GpuColumnVector.combineColumns(nullBatch, streamBatch)
+        }
+      }
+    }
+  }
+
+  private def buildNullBatch(numRows: Int): ColumnarBatch = {
+    val cols: Array[ColumnVector] = buildTypes.safeMap { dt =>
+      GpuColumnVector.fromNull(numRows, dt)
+    }
+    new ColumnarBatch(cols, numRows)
+  }
+}