The primary goal of any educational institution is to provide students with a high-quality learning experience and comprehensive knowledge. Identifying students who need additional support and implementing effective strategies to enhance their academic performance is critical to achieving this objective. This study applies three machine learning techniques to develop a predictive model for assessing student performance across various academic disciplines and institutions. The techniques include Logistic Regression, k-Nearest Neighbours (KNN), and Support Vector Machine (SVM). The models were evaluated using metrics such as the Receiver Operating Characteristic (ROC) index and classification accuracy. Additional performance indicators, including classification error, precision, recall, and the F1-score were also computed. The dataset, which consists of data from a student survey and academic records, included information from a total of 700 + students. Among the models tested, the SVM model outperformed the others, achieving an ROC index of 0.82 and a classification accuracy of 84.04%.