Under contemporary lifestyle pressures, university students widely face various health issues manifested primarily through exam-induced sleep deprivation leading to irregular sleep patterns, dietary imbalances arising from reliance on oily/salty cafeteria meals or frequent takeout, and physical inactivity characterized by prolonged sedentary behavior in dormitories. Existing health management tools fail to meet personalized needs due to functional limitations—typically supporting only exercise tracking—and inadequate contextual adaptation, particularly reflected in the incompatibility between high-end fitness plans and dormitory environments. This study develops a Python-based health management system utilizing the Flask framework, which integrates Baidu's OCR API for dietary monitoring and machine learning techniques for weight prediction. This intelligent context-aware solution effectively lowers the implementation barriers to health management for students.