Higher education institutions must cultivate a technically skilled workforce to meet increasing job demands. However, graduation rates remain low, with 40% of first-year students not completing their degrees. This research proposes an integrated system using machine learning, particularly neural networks, to predict student success based on multifaceted interactions. The model demonstrates a systematic approach to improving degree completion outcomes.