With the growing emphasis on data-driven decision-making, predicting student academic performance has become essential for effective academic planning. This study introduces a machine learning-based approach to forecasting Cumulative Grade Point Average (CGPA) by analyzing semester-wise GPA trends from a student’s first semester. By leveraging predictive analytics, the proposed model helps educators gain early insights into students’ academic progress, enabling timely interventions, tailored learning strategies, and efficient resource allocation. This approach is designed to cater to individual student needs while improving overall institutional performance. Furthermore, the model assists educational institutions in refining curricula and teaching methods based on observed academic patterns. The combination of machine learning and predictive analytics demonstrates its potential in transforming academic support systems and enhancing student success.