Smartphone-based digital burnout detection system using multimodal data fusion from mental health, mobile usage, sleep, and fatigue datasets. Four input datasets flow through random sampling fusion, then branch into four engineered features—Digital Overload Index, Stress Score, Recovery Score, and Distraction Ratio—which combine into a burnout score formula (B = D + T - R + Q). The fused dataset undergoes feature scaling and SMOTE class balancing before LightGBM classification produces three risk categories: Low, Moderate, and High burnout. This architecture demonstrates end-to-end ML pipeline design for wearable and behavioral health prediction, combining domain-specific feature engineering with industry-standard preprocessing and gradient boosting. Fork and customize this diagram on Diagrams.so to adapt burnout detection logic for your health tech platform or research application.