The main objective of any educational institution is to provide students with the best education and knowledge possible. To do this, it is crucial to recognize the kids needing more help and take the necessary steps to raise their performance. In this chapter, we predicted how well students would perform on their GATE test on the first attempt. The linear regression machine learning (ML) model is our primary method for predicting the GATE score. We individually trained the models for each GATE CS paper’s subjects. The earlier graduation marks for each topic are given special regard in this research. Now that we have analyzed our ML models for each subject based on their training and testing accuracy, we can conclude that the models for every individual subject give an accuracy of more than 90%. Hence, our approach and model can be implemented in real-time applications.