Creating learning environments, where students, parents, and teachers are linked to a learning process, helps study their overall impact on the students’ performance. Data mining can analyze these inter-relationships and thus enable the prediction of academic performance to improve the student’s academic level. The main factors that affect the student’s performance were selected using feature selection methods. An analysis of the crucial features was investigated to better understand the data. One of the main outcomes found is the impact of the behavioral features on the students’ academic performance. Moreover, gender and relation demographical features are another important features found. It was evedent that there is an academic disparity between genders, as females constitute the most outstanding students. Furthermore, mothers have a clear role in student academic excellence. Six machine learning methods were used and tested to predict the studnet’s performance, namely random forest, logistic regression, XGBoost, MLP, and ensemble learning using bagging and voting. Of all the methods, the random forest got the highest accuracy with 10-best selected features that reached 77%. Overfitting was addressed successfully by tuning the hyper-parameters. The results show that data mining can accurately predict the students’ performance level, as well as highlight the most influential features.