Apparently, most life activities that people perform depend on their unique characteristics. Personal characteristics vary across people, so they perform tasks in different ways based on their skills. People have different mental, psychological, and behavioral features that affect most life activities. This is the same case with students at various educational levels. Students have different features that affect their academic performance. The academic score is the main indicator of the student's performance. However, other factors such as personality features, intelligence level, and basic personal data can have a great influence on the student's performance. This means that the academic score is not the only indicator that can be used in predicting students' performance. Consequently, an approach based on personal data, personality features, and intelligence quotient is proposed to predict the performance of university undergraduates. Five machine learning techniques were used in the proposed approach. In order to evaluate the performance of the proposed approach, a real student's dataset was used, and various performance measures were computed. Several experiments were performed to determine the impact of various features on the student's performance. The proposed approach gave promising results when tested on the dataset.