diff --git a/fe/fe-core/src/main/java/org/apache/doris/nereids/stats/FilterEstimation.java b/fe/fe-core/src/main/java/org/apache/doris/nereids/stats/FilterEstimation.java
index 2839a817700794..1628c3b7d72e3f 100644
--- a/fe/fe-core/src/main/java/org/apache/doris/nereids/stats/FilterEstimation.java
+++ b/fe/fe-core/src/main/java/org/apache/doris/nereids/stats/FilterEstimation.java
@@ -67,6 +67,9 @@
  */
 public class FilterEstimation extends ExpressionVisitor<Statistics, EstimationContext> {
     public static final double DEFAULT_INEQUALITY_COEFFICIENT = 0.5;
+    // "Range selectivity is prone to producing outliers, so we add this threshold limit.
+    // The threshold estimation is calculated based on selecting one month out of fifty years."
+    public static final double RANGE_SELECTIVITY_THRESHOLD = 0.0016;
     public static final double DEFAULT_IN_COEFFICIENT = 1.0 / 3.0;
 
     public static final double DEFAULT_LIKE_COMPARISON_SELECTIVITY = 0.2;
@@ -600,6 +603,8 @@ private Statistics estimateBinaryComparisonFilter(Expression leftExpr, DataType
             double sel = leftRange.overlapPercentWith(rightRange);
             if (!(dataType instanceof RangeScalable) && (sel != 0.0 && sel != 1.0)) {
                 sel = DEFAULT_INEQUALITY_COEFFICIENT;
+            } else if (sel < RANGE_SELECTIVITY_THRESHOLD) {
+                sel = RANGE_SELECTIVITY_THRESHOLD;
             }
             sel = getNotNullSelectivity(leftStats, sel);
             updatedStatistics = context.statistics.withSel(sel);
diff --git a/fe/fe-core/src/test/java/org/apache/doris/nereids/stats/FilterEstimationTest.java b/fe/fe-core/src/test/java/org/apache/doris/nereids/stats/FilterEstimationTest.java
index d7c44e082cf52e..0158dd9587c70b 100644
--- a/fe/fe-core/src/test/java/org/apache/doris/nereids/stats/FilterEstimationTest.java
+++ b/fe/fe-core/src/test/java/org/apache/doris/nereids/stats/FilterEstimationTest.java
@@ -34,6 +34,7 @@
 import org.apache.doris.nereids.trees.expressions.Or;
 import org.apache.doris.nereids.trees.expressions.SlotReference;
 import org.apache.doris.nereids.trees.expressions.functions.scalar.Left;
+import org.apache.doris.nereids.trees.expressions.literal.BigIntLiteral;
 import org.apache.doris.nereids.trees.expressions.literal.DateLiteral;
 import org.apache.doris.nereids.trees.expressions.literal.DoubleLiteral;
 import org.apache.doris.nereids.trees.expressions.literal.IntegerLiteral;
@@ -1365,4 +1366,32 @@ public void testStringRangeColToCol() {
         Statistics agrtc = new FilterEstimation().estimate(new GreaterThan(a, c), baseStats);
         Assertions.assertEquals(50, agrtc.getRowCount());
     }
+
+    @Test
+    public void testLargeRange() {
+        SlotReference a = new SlotReference("a", IntegerType.INSTANCE);
+        long tenB = 1000000000;
+        long row = 1600000000;
+        ColumnStatistic colStats = new ColumnStatisticBuilder()
+                .setAvgSizeByte(10)
+                .setCount(row)
+                .setNdv(10000)
+                .setMinExpr(new IntLiteral(0))
+                .setMinValue(0)
+                .setMaxExpr(new IntLiteral(tenB))
+                .setMaxValue(tenB)
+                .build();
+        Statistics stats = new StatisticsBuilder()
+                .setRowCount(row)
+                .putColumnStatistics(a, colStats)
+                .build();
+        Expression less = new LessThan(a, new IntegerLiteral(50000));
+        FilterEstimation estimation = new FilterEstimation();
+        Statistics out = estimation.estimate(less, stats);
+        Assertions.assertEquals(out.getRowCount(), row * FilterEstimation.RANGE_SELECTIVITY_THRESHOLD);
+
+        Expression greater = new GreaterThan(a, new BigIntLiteral(tenB - 5000L));
+        out = estimation.estimate(greater, stats);
+        Assertions.assertEquals(out.getRowCount(), row * FilterEstimation.RANGE_SELECTIVITY_THRESHOLD);
+    }
 }