Student Performance Prediction Using Machine Learning: A Systematic Review

With the rapid growth of digital learning platforms and online education systems, educational institutions generate large amounts of student-related data. Predicting student academic performance using Machine Learning (ML) techniques has become an important research area in educational data mining. Traditional methods of evaluating student performance are often limited to examination results and manual analysis, which may fail to identify at-risk students at an early stage. This paper presents a systematic review of Machine Learning techniques used for student performance prediction. It analyzes different ML approaches including Decision Trees, Support Vector Machines (SVM), Random Forest, Artificial Neural Networks (ANN), and Deep Learning models for predicting academic outcomes. The study also discusses applications such as dropout prediction, grade forecasting, attendance analysis, and personalized learning systems. The review identifies key advantages, challenges, and research gaps in existing studies. Finally, it suggests future directions for developing intelligent educational systems that can improve student success rates and learning experiences.