ResQ - AI Powered Disaster Management and Resource Allocation System

Abstract

Natural disasters—including floods, cyclones, earthquakes, and wildfires—continue to cause extensive loss of life and infrastructure, highlighting the need for intelligent, fast, and coordinated disaster response mechanisms. In recent years, Artificial Intelligence (AI) has emerged as a transformative tool for enhancing disaster preparedness, response, and recovery through data-driven decision-making. This review examines recent research (2022–2025) on the application of machine learning, deep learning, generative AI, multimodal data fusion, UAV and satellite imagery analysis, blockchain-based transparency, and optimization-driven decision support in disaster management systems. The surveyed studies demonstrate notable improvements in hazard forecasting accuracy, rapid damage assessment, situational awareness, and efficiency of relief distribution. However, several challenges remain unresolved, including limited real-time data integration, lack of interoperability among agencies, insufficient automation, and fragmented system architectures that separate predictive models from operational resource planning. This paper consolidates existing findings, identifies key research gaps, and discusses future directions toward the development of unified, scalable, and adaptive AI-driven disaster management frameworks capable of supporting end-to-end emergency operations.