Background

Educational institutions increasingly generate large volumes of student-related data through learning management systems, online assessments, attendance records, and academic databases. Machine Learning (ML) has emerged as a powerful tool for analyzing these datasets and predicting student academic performance, enabling educators to identify at-risk learners and implement timely interventions.

Objective

This study explores machine learning applications in student performance prediction, examines commonly used algorithms, evaluates predictive factors, identifies implementation challenges, and discusses future directions in educational analytics.

Methods

A narrative review methodology was employed using studies published between 2018 and 2025. Relevant literature was obtained from Scopus, Web of Science, IEEE Xplore, ACM Digital Library, ERIC, and Google Scholar. Research focusing on machine learning models, educational data mining, learning analytics, and student performance prediction was included.

Results

Machine learning techniques including Decision Trees, Random Forests, Support Vector Machines, Artificial Neural Networks, Gradient Boosting, and Deep Learning demonstrated strong predictive capabilities. Key predictors included attendance, prior academic achievement, engagement metrics, socioeconomic factors, and online learning behaviors. Random Forest and Gradient Boosting models frequently achieved prediction accuracies exceeding 85%.

Conclusion

Machine learning provides valuable opportunities for enhancing educational decision-making and supporting student success. Effective implementation requires quality data, ethical governance, transparency, and collaboration among educators, administrators, and data scientists.