A novel academic performance early warning model was developed tailored for vocational colleges, leveraging the capabilities of the XGBoost algorithm. Big data and machine learning have provided new opportunities for educational institutions to predict student outcomes and identify at-risk students early. The model incorporates a variety of student data, including attendance, grades, and behavioral indicators, to generate accurate predictions regarding academic success. Using a dataset from a vocational college, the model identifies students who are likely to underperform for timely interventions. Compared to traditional models, XGBoost offers superior accuracy and efficiency, making it a valuable tool for real-time academic performance monitoring. The application of such predictive models can significantly enhance student retention and academic achievement by enabling institutions to address academic challenges proactively.