FlowSense AI is a full-stack emergency-response platform that combines virtual IoT traffic sensing, real-time analytics, and AI-driven route optimization to reduce ambulance delays in congested cities. The solution continuously ingests traffic telemetry, computes congestion severity, estimates optimized dispatch routes, and evaluates both medical and environmental outcomes through survival and eco-impact models. Experimental results demonstrate an average response time reduction of 35%, from 13–15 minutes to 8–11 minutes, with emergency type-specific improvements ranging from 3.4 to4.3 minutes saved per dispatch. Golden Hour compliance rates improved significantly across all emergency categories, and estimated patient survival probabilities increased by up to 7.2%. The architecture integrates a Python sensor simulator, a Flask backend with MongoDB and Redis, and a React dashboard with live geospatial visualization. This paper documents the

platform’s design, methodology, system architecture, implementation, results analysis, and future deployment pathway for scalable smart-city emergency response