Predicting student academic performance is vital for enabling educational institutions to provide early interventions and enhance learning outcomes. This study presents a comparative analysis of machine learning (ML) algorithms for classifying student performance using a hybrid dataset consisting of academic records and socio-demographic attributes. The dataset includes 97 undergraduate computer science students and features such as GPA, attendance, parental education, part-time employment, and extracurricular activities. The data underwent preprocessing steps including label encoding, feature scaling, and feature selection. GPA values were discretized into four grade categories (A, B, C, D) to serve as the target class. Four ML classification algorithms—Random Forest, Support Vector Machine, Decision Tree, and AdaBoost—were evaluated using accuracy, precision, recall, and F1-score. Among the models tested, AdaBoost achieved the highest accuracy of 85%, along with strong precision and recall scores, making it the most effective algorithm for the given dataset. The Decision Tree and Random Forest also showed reasonable performance, whereas the Support Vector Machine had the lowest scores, particularly in terms of precision. Feature importance analysis highlighted absences, gender, and assignment time as the most influential predictors. These findings suggest that ensemble models are well-suited for academic performance prediction and can support data-driven decision-making in higher education.