The effective allocation of emergency supplies is crucial in the aftermath of flood disasters, as it directly impacts response times and mitigates casualties and property losses. Traditional methods of material distribution predominantly rely on ground-based transportation, which often proves inefficient and inflexible under the dynamic conditions of a disaster. This study explores the potential of unmanned aerial vehicles (UAVs) as a transformative solution to the challenges associated with emergency material dispatch. Factors influencing UAV scheduling, including environmental constraints, payload capacity, and flight dynamics, are analyzed in depth. Optimization measures for improving UAV collaborative operations are proposed, with a focus on enhancing the efficiency and adaptability of disaster response systems. The integration of reinforcement learning (RL) is examined as a theoretical framework for optimizing UAV collaborative scheduling, facilitating autonomous decision-making in real-time scenarios. An empirical analysis is presented based on the “7-20” rainstorm and flooding disaster in Zhengzhou, illustrating the practical application of collaborative UAVs in disaster relief. The results demonstrate the significant optimization potential of UAV technology, with a notable reduction in response times and improved logistical coordination. Furthermore, the role of UAVs in future disaster relief operations is discussed, with emphasis on the integration of blockchain and smart dispatch systems to enable decentralized, autonomous coordination. These advancements are expected to enhance the overall efficiency of emergency material distribution and better address the complex challenges posed by post-disaster environments. The findings underscore the potential for UAV systems to revolutionize disaster management and contribute to more resilient, responsive strategies in future flood events.