Reducing Urban Emergency Fatalities: A Holistic AI-Driven Rescue Model

Emergency response systems are critical for saving lives during crises, yet traditional methods often suffer from delays due to fragmented communication and resource mismanagement. This paper proposes a Rescue Squad Web Project (RSWP), a unified platform connecting individuals in emergencies with nearby rescue teams equipped with appropriate tools. The system integrates geolocation tracking, real-time databases, and AI-driven emergency classification to optimize response times and resource allocation. By leveraging GPS and crowdsourced data, RSWP ensures that the nearest available squad receives instant alerts with contextual details, such as emergency type and required instruments. A literature review highlights advancements in IoT, machine learning, and real-time systems, while identifying gaps in holistic emergency management solutions. The proposed methodology emphasizes modular architecture, AI-based prioritization, and multi-stakeholder coordination. Future enhancements could include drone integration and predictive analytics. This project aims to reduce fatalities by 30–40% in urban emergencies, as evidenced by simulations