diff --git a/asv_benchmarks/benchmarks/ensemble.py b/asv_benchmarks/benchmarks/ensemble.py
index c336d1e5f8805..a519cece3ac27 100644
--- a/asv_benchmarks/benchmarks/ensemble.py
+++ b/asv_benchmarks/benchmarks/ensemble.py
@@ -2,6 +2,7 @@
     GradientBoostingClassifier,
     HistGradientBoostingClassifier,
     RandomForestClassifier,
+    RandomForestRegressor
 )
 
 from .common import Benchmark, Estimator, Predictor
@@ -9,8 +10,50 @@
     _20newsgroups_highdim_dataset,
     _20newsgroups_lowdim_dataset,
     _synth_classification_dataset,
+    _synth_regression_dataset,
+    _synth_regression_sparse_dataset
 )
-from .utils import make_gen_classif_scorers
+from .utils import make_gen_classif_scorers, make_gen_reg_scorers
+
+
+class RandomForestRegressorBenchmark(Predictor, Estimator, Benchmark):
+    """
+    Benchmarks for RandomForestRegressor.
+    """
+
+    param_names = ["representation", "n_jobs"]
+    params = (["dense", "sparse"], Benchmark.n_jobs_vals)
+
+    def setup_cache(self):
+        super().setup_cache()
+
+    def make_data(self, params):
+        representation, n_jobs = params
+
+        if representation == "sparse":
+            data = _synth_regression_sparse_dataset()
+        else:
+            data = _synth_regression_dataset()
+
+        return data
+
+    def make_estimator(self, params):
+        representation, n_jobs = params
+
+        n_estimators = 500 if Benchmark.data_size == "large" else 100
+
+        estimator = RandomForestRegressor(
+            n_estimators=n_estimators,
+            min_samples_split=10,
+            max_features="log2",
+            n_jobs=n_jobs,
+            random_state=0,
+        )
+
+        return estimator
+
+    def make_scorers(self):
+        make_gen_reg_scorers(self)
 
 
 class RandomForestClassifierBenchmark(Predictor, Estimator, Benchmark):