Real-Time Emergency Response System Using Geolocation, Data Analytics, and Machine Learning
Dr.Vikas Singhal · International Journal of Innovative Research in Advanced Engineering · 2025
In critical emergency situations, rapid response can significantly reduce casualties and damage. This research proposes an intelligent emergency response system that leverages geolocation APIs and machine learning to optimize dispatch and resource allocation. By integrating real-time location data from users and emergency services, the system accurately identifies incident locations and dynamically selects the nearest and most suitable emergency units. Machine learning models are employed to predict incident severity, estimate response times, and continuously improve decision-making based on historical data. The proposed system demonstrates improved efficiency, reduced response times, and enhanced situational awareness, offering a scalable and adaptive solution for modern emergency management infrastructures. A GPS (Global Positioning System) modem embedded in the ambulance allows the hospital’s portal servers to continuously track its live location. WebRTC (Web Real-Time Communications) live video streaming provides real-time doctor-patient interaction during emergencies, enabling seamless audio and video communication.