The education process plays a pivotal role in shaping a generation capable of leading a country toward development in all aspects of life. The emergence and convergence of Machine learning, cloud computing, and ANN have empowered researchers to build models that target to imitate various aspects of human intelligence. Early performance prediction enables teachers to carry out prompt interventions and give extra help to pupils who don’t do well and might be at risk of falling behind. By leveraging the capabilities of machine learning, educators can transform teaching and learning experiences, pinpoint areas for enhancement, and significantly boost student success. In this study, the researcher examined the capabilities of three classifiers: Support Vector Machines (SVM), KNN, and Logistic Regression. The results showed that adjusting the parameters significantly increased the accuracy of all three prediction methods. The SVM (Support Vector Machine) method demonstrated the best prediction accuracy, recall and F1 score.