This research presents a proposed approach that could be applied in modeling students' study strategies and performance in higher education. The research used key learning attributes, including intrinsic motivation, extrinsic motivation, autonomy, relatedness, competence, and self-esteem in the modeling. Five machine learning models were implemented, trained, evaluated, and tested with data from 924 university students. The comparative analysis reveals that tree-based models, particularly random forest and decision trees, outperform other models, achieving a prediction accuracy of 94.9%. The models built in this research can be used in predicting student study strategies and performance and this can be applied in implementing targeted interventions for improving learning progress. The research findings emphasize the importance of incorporating strategies that address diverse motivation dimensions in online educational systems, as it increases student engagement and promotes continuous learning. The findings also highlight the potential for modeling these attributes collectively to personalize and adapt learning process.