This study addresses the lack of disaster response systems for medical and biological emergencies, aiming to develop a GIS-based platform for effective response to infectious diseases. The platform integrates pathogen detection, risk assessment, epidemiological investigation, and situational awareness using artificial intelligence and big data technologies. It leverages satellite and sensor data to analyze water pollution, providing real-time insights on contamination levels and affected areas. The system comprises seven layers for data management, interoperability, application, and security, enabling users to access geographic information and statistics. The platform's effectiveness was demonstrated through pilot case studies. In Limassol, flash floods polluted a reservoir, and the platform tracked pollution levels over time using satellite imagery and sensor data. In a Korean case, African swine fever spread near a river due to high precipitation. The platform helped assess ASF cases, plan containment strategies, and monitor tap water quality in real-time. The platform's AI-driven information database enhances infectious disease response, empowering first responders with quick detection and informed decision-making. As the system evolves and data analysis deepens, its potential for reducing industrial accidents through pattern recognition and machine learning becomes apparent.