In the era of digital transformation, educational institutions are increasingly leveraging data analytics to enhance academic outcomes and improve decision-making. Predictive analytics, powered by machine learning (ML), provides a systematic approach to forecast student performance and identify at-risk learners early. This research investigates the application of ML algorithms such as Decision Trees, Random Forest, Support Vector Machines (SVM), and Neural Networks to predict student performance using academic, demographic, and behavioral datasets. The study explores how different features—such as attendance, internal assessments, participation in learning activities, and socio-economic factors—contribute to prediction accuracy. The experimental analysis reveals that ensemble learning methods outperform traditional classifiers, achieving accuracy above 90% on benchmark datasets. The findings suggest that predictive analytics can serve as a proactive tool for personalized learning and timely intervention, thus enhancing institutional performance and student retention rates.