Emergency Route Optimization During Disasters Using AI And Iot: A Comprehensive Review of Sensing Approaches, Routing Algorithms, Report-ed Evidence, and Deployment Challenges

Abstract

Disasters such as floods, earthquakes and cyclones damage roads and delay ambulances, fire brigades and rescue teams, while conventional navigation relies on static maps that ignore flooding, blockages and congestion. This paper presents a focused narrative review that synthesises five studies on AI-based emergency and evacuation routing: a decentralized reinforcement-learning framework that couples Dijkstra-style routing with traffic-signal control; a machine-learning recommendation system that predicts congestion for ambulances; a flood-sensor system that converts water-level readings into dynamic edge weights for evacuation routing; a graph-based method that reconfigures road directions in real time; and a hybrid quantum machine-learning approach to escape routing. The review examines three connected themes: sensing approaches and routing models, the strength and comparability of the reported evidence, and the conditions that matter when routing is extended into a full IoT-to-dashboard pipeline. All five studies were assessed from their published abstracts and available excerpts, not read in full; the one headline figure found is a 74% improvement in evacuation efficiency, and it cannot be ranked against the other results, which are mostly qualitative. Only one study links physical sensor readings to routing. The review contributes a comparative table, a conceptual architecture for the proposed system, which is not experimentally validated, and a prioritised agenda of research gaps.