diff --git a/sklearn_genetic/tests/test_mlflow.py b/sklearn_genetic/tests/test_mlflow.py
new file mode 100644
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+++ b/sklearn_genetic/tests/test_mlflow.py
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+import pytest
+import mlflow
+import shutil
+import os
+from urllib.parse import urlparse
+from mlflow.tracking import MlflowClient
+from mlflow.entities import ViewType
+
+from sklearn.datasets import load_digits
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import StratifiedKFold
+
+from ..genetic_search import GASearchCV
+from ..mlflow import MLflowConfig
+from ..space import Integer, Categorical, Continuous
+
+
+@pytest.fixture
+def mlflow_resources():
+    uri = mlflow.get_tracking_uri()
+    client = MlflowClient(uri)
+    return (uri, client)
+
+
+@pytest.fixture
+def mlflow_run(mlflow_resources):
+    _, client = mlflow_resources
+    exp_id = client.get_experiment_by_name("Digits-sklearn-genetic-opt").experiment_id
+    active_run = mlflow.list_run_infos(exp_id, run_view_type=ViewType.ACTIVE_ONLY)
+    runs = [run.run_id for run in active_run]
+    return runs
+
+
+def test_mlflow_config(mlflow_resources):
+    """
+    Check MLflow config creation.
+    """
+    uri, _ = mlflow_resources
+    mlflow_config = MLflowConfig(
+        tracking_uri=uri,
+        experiment="Digits-sklearn-genetic-opt",
+        run_name="Decision Tree",
+        save_models=True,
+        tags={"team": "sklearn-genetic-opt", "version": "0.5.0"},
+    )
+    assert isinstance(mlflow_config, MLflowConfig)
+
+
+def test_runs(mlflow_resources, mlflow_run):
+    """
+    Check if runs are captured and parameters are true.
+    """
+    uri, client = mlflow_resources
+    mlflow_config = MLflowConfig(
+        tracking_uri=uri,
+        experiment="Digits-sklearn-genetic-opt",
+        run_name="Decision Tree",
+        save_models=True,
+        tags={"team": "sklearn-genetic-opt", "version": "0.5.0"},
+    )
+
+    clf = DecisionTreeClassifier()
+
+    data = load_digits()
+    label_names = data["target_names"]
+    y = data["target"]
+    X = data["data"]
+
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.33, random_state=42
+    )
+
+    params_grid = {
+        "min_weight_fraction_leaf": Continuous(0, 0.5),
+        "criterion": Categorical(["gini", "entropy"]),
+        "max_depth": Integer(2, 20),
+        "max_leaf_nodes": Integer(2, 30),
+    }
+
+    cv = StratifiedKFold(n_splits=3, shuffle=True)
+
+    evolved_estimator = GASearchCV(
+        clf,
+        cv=cv,
+        scoring="accuracy",
+        population_size=3,
+        generations=5,
+        tournament_size=3,
+        elitism=True,
+        crossover_probability=0.9,
+        mutation_probability=0.05,
+        param_grid=params_grid,
+        algorithm="eaMuPlusLambda",
+        n_jobs=-1,
+        verbose=True,
+        log_config=mlflow_config,
+    )
+
+    evolved_estimator.fit(X_train, y_train)
+    y_predict_ga = evolved_estimator.predict(X_test)
+    accuracy = accuracy_score(y_test, y_predict_ga)
+    runs = mlflow_run
+    assert len(runs) >= 1 and evolved_estimator.best_params_["min_weight_fraction_leaf"]
+
+
+def test_mlflow_artifacts(mlflow_resources, mlflow_run):
+    _, client = mlflow_resources
+    run_id = mlflow_run[0]
+    run = client.get_run(run_id)
+    assert client.list_artifacts(run_id)[0].path == "model"
+
+
+def test_mlflow_params(mlflow_resources, mlflow_run):
+    """
+    Test parameters are all in the run and within range.
+    """
+    _, client = mlflow_resources
+    run_id = mlflow_run[0]
+    run = client.get_run(run_id)
+    params = run.data.params
+
+    assert 0 <= float(params["min_weight_fraction_leaf"]) <= 0.5
+    assert params["criterion"] == "gini" or "entropy"
+    assert 2 <= int(params["max_depth"]) <= 20
+    assert 2 <= int(params["max_leaf_nodes"]) <= 30
+
+
+def test_mlflow_after_run(mlflow_resources, mlflow_run):
+    """
+    Check the end of the runs are logged artifacts/metric/hyperparameters exists in the mlflow server
+    """
+    run_id = mlflow_run[0]
+    mlflow.end_run()
+    _, client = mlflow_resources
+    run = client.get_run(run_id)
+    params = run.data.params
+
+    assert 0 <= float(params["min_weight_fraction_leaf"]) <= 0.5
+    assert params["criterion"] == "gini" or "entropy"
+    assert 2 <= int(params["max_depth"]) <= 20
+    assert 2 <= int(params["max_leaf_nodes"]) <= 30
+    assert client.get_metric_history(run_id, "score")[0].key == "score"
+
+
+def test_cleanup():
+    """
+    Ensure resources are cleaned up.
+    """
+    shutil.rmtree("mlruns")
+    assert "mlruns" not in os.listdir(os.getcwd())