The water scarcity crisis in Tehran Province and the Varamin Plain, coupled with frequent water and power outages, has led to air entrainment in pipelines, increased pressure, and pipe bursts, highlighting the need for intelligent water management in the deployment of emergency response teams. The innovation of this study lies in applying artificial intelligence–based methods for spatial analysis of incidents and identifying optimal locations for the deployment of mobile emergency teams. The main focus is on reducing response time and improving service coverage through the determination of optimal points using the Self-Organizing Map algorithm. This innovative approach can contribute to the development of integrated software for incident management and faster decision-making under emergency conditions. The study area covers four counties: Varamin, Pishva, Qarchak, and Pakdasht. Using the Self-Organizing Feature Mapneural network algorithm, 16 optimal points for the deployment of mobile emergency teams were identified to enhance coverage and reduce response time, thereby minimizing losses caused by water and power disruptions. A total of 3,603 incident points recorded in the GIS system with UTM coordinates (Zone 39N) were used for spatial analysis and clustering in MATLAB. The research process included spatial data collection and preprocessing, SOM execution, and output map generation in the GIS environment. Among the 16 optimal locations, 9 are situated in high-incident-density areas (more than 8 incidents per square kilometer), and 6 are located in zones with high customer density (over 1,666 customers per square kilometer). The distribution of other points across lower-density zones ensures adequate coverage of rural and sparsely populated areas. The results indicate that the SOM algorithm successfully identified spatial patterns of incidents and population density, achieving balanced and efficient site selection for mobile emergency teams. The main advantage of SOM lies in its ability to analyze two-dimensional spatial data precisely, preserve topological structure, and adapt to data variability-making it superior to other clustering and metaheuristic methods. The findings confirm that the SOM algorithm is an effective approach for urban crisis management and optimal deployment of emergency resources, with potential for further development using more complex datasets to enhance rapid response systems.