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Student performance prediction has become an important research area in educational data mining. This project proposes a Machine Learning–based system that analyzes academic and behavioral data to predict student performance in advance. The system uses algorithms such as Decision Tree, Random Forest, and Logistic Regression to classify students into performance categories (High, Medium, and Low) or predict final grades. The dataset includes attributes such as attendance, internal marks, assignment scores, study hours, and previous semester results. The trained model identifies at-risk students early and provides actionable insights for teachers and institutions. The proposed system improves academic monitoring, enables early intervention, and enhances overall student success rates through data-driven decision making.