diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index 4044c0878921..46810bccc8e6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -342,9 +342,6 @@ class GBTClassificationModel private[ml](
     }
   }
 
-  /** Number of trees in ensemble */
-  val numTrees: Int = trees.length
-
   @Since("1.4.0")
   override def copy(extra: ParamMap): GBTClassificationModel = {
     copyValues(new GBTClassificationModel(uid, _trees, _treeWeights, numFeatures, numClasses),
@@ -353,7 +350,7 @@ class GBTClassificationModel private[ml](
 
   @Since("1.4.0")
   override def toString: String = {
-    s"GBTClassificationModel: uid = $uid, numTrees=$numTrees, numClasses=$numClasses, " +
+    s"GBTClassificationModel: uid = $uid, numTrees=$getNumTrees, numClasses=$numClasses, " +
       s"numFeatures=$numFeatures"
   }
 
@@ -374,7 +371,7 @@ class GBTClassificationModel private[ml](
   /** Raw prediction for the positive class. */
   private def margin(features: Vector): Double = {
     val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
-    blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
+    blas.ddot(getNumTrees, treePredictions, 1, _treeWeights, 1)
   }
 
   /** (private[ml]) Convert to a model in the old API */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index 29991f59e37c..2c2558f00bb1 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -300,12 +300,9 @@ class GBTRegressionModel private[ml](
     // TODO: When we add a generic Boosting class, handle transform there?  SPARK-7129
     // Classifies by thresholding sum of weighted tree predictions
     val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
-    blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
+    blas.ddot(getNumTrees, treePredictions, 1, _treeWeights, 1)
   }
 
-  /** Number of trees in ensemble */
-  val numTrees: Int = trees.length
-
   @Since("1.4.0")
   override def copy(extra: ParamMap): GBTRegressionModel = {
     copyValues(new GBTRegressionModel(uid, _trees, _treeWeights, numFeatures),
@@ -314,7 +311,7 @@ class GBTRegressionModel private[ml](
 
   @Since("1.4.0")
   override def toString: String = {
-    s"GBTRegressionModel: uid=$uid, numTrees=$numTrees, numFeatures=$numFeatures"
+    s"GBTRegressionModel: uid=$uid, numTrees=$getNumTrees, numFeatures=$numFeatures"
   }
 
   /**
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
index abeb4b5331ac..a2208edcb839 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
@@ -179,7 +179,8 @@ class GBTClassifierSuite extends MLTest with DefaultReadWriteTest {
         assert(raw.size === 2)
         // check that raw prediction is tree predictions dot tree weights
         val treePredictions = gbtModel.trees.map(_.rootNode.predictImpl(features).prediction)
-        val prediction = blas.ddot(gbtModel.numTrees, treePredictions, 1, gbtModel.treeWeights, 1)
+        val prediction = blas.ddot(gbtModel.getNumTrees, treePredictions, 1,
+          gbtModel.treeWeights, 1)
         assert(raw ~== Vectors.dense(-prediction, prediction) relTol eps)
 
         // Compare rawPrediction with probability
@@ -436,9 +437,9 @@ class GBTClassifierSuite extends MLTest with DefaultReadWriteTest {
       gbt.setValidationIndicatorCol(validationIndicatorCol)
       val modelWithValidation = gbt.fit(trainDF.union(validationDF))
 
-      assert(modelWithoutValidation.numTrees === numIter)
+      assert(modelWithoutValidation.getNumTrees === numIter)
       // early stop
-      assert(modelWithValidation.numTrees < numIter)
+      assert(modelWithValidation.getNumTrees < numIter)
 
       val (errorWithoutValidation, errorWithValidation) = {
         val remappedRdd = validationData.map {
@@ -457,10 +458,10 @@ class GBTClassifierSuite extends MLTest with DefaultReadWriteTest {
           modelWithoutValidation.treeWeights, modelWithoutValidation.getOldLossType,
           OldAlgo.Classification)
       assert(evaluationArray.length === numIter)
-      assert(evaluationArray(modelWithValidation.numTrees) >
-        evaluationArray(modelWithValidation.numTrees - 1))
+      assert(evaluationArray(modelWithValidation.getNumTrees) >
+        evaluationArray(modelWithValidation.getNumTrees - 1))
       var i = 1
-      while (i < modelWithValidation.numTrees) {
+      while (i < modelWithValidation.getNumTrees) {
         assert(evaluationArray(i) <= evaluationArray(i - 1))
         i += 1
       }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
index 35c0fc9b02b1..04b0d4b8470f 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
@@ -274,9 +274,9 @@ class GBTRegressorSuite extends MLTest with DefaultReadWriteTest {
       gbt.setValidationIndicatorCol(validationIndicatorCol)
       val modelWithValidation = gbt.fit(trainDF.union(validationDF))
 
-      assert(modelWithoutValidation.numTrees === numIter)
+      assert(modelWithoutValidation.getNumTrees === numIter)
       // early stop
-      assert(modelWithValidation.numTrees < numIter)
+      assert(modelWithValidation.getNumTrees < numIter)
 
       val errorWithoutValidation = GradientBoostedTrees.computeWeightedError(
         validationData.map(_.toInstance),
@@ -294,10 +294,10 @@ class GBTRegressorSuite extends MLTest with DefaultReadWriteTest {
           modelWithoutValidation.treeWeights, modelWithoutValidation.getOldLossType,
           OldAlgo.Regression)
       assert(evaluationArray.length === numIter)
-      assert(evaluationArray(modelWithValidation.numTrees) >
-        evaluationArray(modelWithValidation.numTrees - 1))
+      assert(evaluationArray(modelWithValidation.getNumTrees) >
+        evaluationArray(modelWithValidation.getNumTrees - 1))
       var i = 1
-      while (i < modelWithValidation.numTrees) {
+      while (i < modelWithValidation.getNumTrees) {
         assert(evaluationArray(i) <= evaluationArray(i - 1))
         i += 1
       }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index f1bbe0b10e22..68e9313805e1 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -344,6 +344,10 @@ object MimaExcludes {
     ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel.layers"),
     ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel.this"),
 
+    // [SPARK-30630][ML] Remove numTrees in GBT
+    ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.ml.classification.GBTClassificationModel.numTrees"),
+    ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.ml.regression.GBTRegressionModel.numTrees"),
+
     // Data Source V2 API changes
     (problem: Problem) => problem match {
       case MissingClassProblem(cls) =>