Abstract
The ability to predict student performance is crucial in understanding the student succession rate. Education is the Power, and by forecasting educational success using the right metrics, we will be able to address student weakness at the appropriate moment by utilising accurate pedagogies and techniques. Various machine learning technologies, including supervised, unsupervised, and reinforcement learning, have been created to predict student performance. Using historical observations, machine learning enables us to learn and generate accurate predictions. In this study, we give a literature review on the topic of predicting student achievement using machine learning techniques, along with the benefits and drawbacks of various machine learning approaches