Educational Data Mining (EDM) uses traditional data mining methods to uncover patterns in educational data, such as student information, educational records, and exam results. A study using machine learning algorithms to predict undergraduate students' final exam grades based on their midterm grades, department, and faculty data serves as an example. This approach achieved a classification accuracy of 70–75%, demonstrating EDM's potential in predicting academic performance and aiding in decision-making processes in higher education. EDM can predict student behaviors, academic achievement, and facilitate the development of pedagogical methods. However, it primarily focuses on quantifiable data, which may overlook factors like student motivation or socio-economic backgrounds. Additionally, EDM's reliance on data quality and algorithmic precision means that its predictions are not infallible and should be used alongside other assessment methods