This　research　presents　a　detailed　survey　of　the　application　of　machine　learning　in　forecasting　student　performance,　highlighting　its　potential　to　enhance　educational　assessments.　While　traditional　assessment　methods　often　suffer　from　subjectivity　and　a　limited　range　of　data,　machine　learning　offers　a　more　objective　and　dynamic　alternative.　The　machine　learning　workflow,　which　includes　feature　engineering,　pre-processing,　data　collection,　and　model　evaluation,　is　described　in　this　study.　This　paper　addresses　the　effectiveness　and　limitations　of　well-known　traditional　models　such　as　Support　Vector　Machines　(SVMs)　and　Naïve　Bayes.　In　addition,　the　study　delves　into　the　relevant　connections　between　Convolutional　Neural　Networks　(CNNs)　and　Artificial　Neural　Networks　(ANNs)　in　improving　student　performance.　The　discussion　section　explores　the　shift　from　manual　analytics　to　machine　learning,　pointing　to　improvements　in　predictive　accuracy　and　efficiency,　as　well　as　challenges　such　as　model　interpretability,　data　quality,　and　privacy　issues.　This　paper　makes　several　suggestions　for　future　directions,　including　integrating　explainable　artificial　intelligence　(XAI)　methods　and　the　Shapley　addition　interpretation　(SHAP)　algorithm　to　improve　transparency,　applying　transfer　learning　to　adapt　models　to　different　educational　contexts,　and　utilising　federated　learning　to　ensure　data　privacy.　The　study＇s　conclusions　are　summed　up　in　the　concluding　section,　which　also　recognizes　the　tremendous　advancements　in　predictive　modeling　and　calls　for　the　continuous　development　of　methods　to　increase　the　models＇　transparency　and　adaptability.　This　will　guarantee　the　ethical　and　successful　application　of　machine　learning　in　educational　settings.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2025.