<![CDATA[Predicting student graduation is an essential component in academic management within higher education institutions. The growing issue of delayed graduations and dropouts (DO) has raised significant concerns in the educational field. By utilizing machine learning methods, predictions regarding student graduation can be made with high accuracy, based on historical data such as academic performance, attendance, background, and social factors. This paper aims to explore various machine learning methods applied in previous studies for predicting student graduation, including Decision Tree, Random Forest, SVM, and Neural Networks. The findings of these studies suggest that models like Random Forest and XGBoost tend to provide the highest accuracy in predicting student outcomes. This review is intended to serve as a foundational reference for the development of data-driven systems for predicting graduation rates in academic environments. ]]>
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