The capacity to forecast student performance has emerged as an essential instrument for educational institutions seeking to promote success and tackle issues preemptively. This work investigates the application of machine learning models, particularly Random Forest classifiers, to classify student final grades into three categories: Low, Medium, and High. The model discovers critical variables, including study time, prior grades, and familial relationships, by examining a dataset comprising academic, demographic, and behavioral factors. The findings indicate elevated classification accuracy, providing meaningful data for educators to customize treatments. This research emphasizes the promise of data-driven methodologies in education while simultaneously highlighting the significance of comprehending the human elements underlying the data. The study enhances the comprehension of student performance by integrating technological improvements with educational objectives.