<p>Wireless Body Sensor Networks (WBSNs) are increasingly used in healthcare, yet few solutions offer a complete end-to-end framework that spans from data collection to proactive intervention. This work addresses that gap by proposing a predictive and preventative system that integrates sensing, data processing, prediction, and emergency response into a unified architecture. To reduce energy consumption, we introduce two novel compression techniques: Range Method (RM) and Change-in-Vital-Sign Method (CVM); which achieve up to 97% reduction in transmitted data. RM outperforms a state-of-the-art protocol by 34.6%, while CVM achieves 6.4% less reduction but offers higher data integrity. For prediction, we design a diagnostic label (DL) that summarizes a patient’s health status and enables accurate forecasting of critical conditions. Our model achieves over 91% accuracy, outperforming the closest baseline (66%), and demonstrates strong precision and recall (F1-score: 0.92). We validate the system’s clinical relevance through a survey of medical professionals: 96% confirmed the value of predicting health deterioration, and 90% emphasized the importance of identifying which vital signs are responsible. Finally, we introduce drones as a new intervention resource. Based on predicted emergencies, the system selects between drones and ambulances for timely response. This approach reduces ambulance usage by up to 90%, offering a scalable and cost-effective enhancement to emergency services. Together, these contributions form a robust end-to-end framework that shifts healthcare from reactive to preventative, improving both patient outcomes and resource efficiency.</p>