<p>Efficient allocation of Internet of Things (IoT) devices and timely real-time data processing in disaster-affected areas pose significant challenges due to energy constraints, limited infrastructure, and the urgent need for rapid response. Unmanned Aerial Vehicles (UAVs), when integrated with Edge Computing (EC), offer a promising solution for collecting and processing data. In this paper, we propose a novel hashing-based quantum-inspired cuckoo search optimization (QICSO) algorithm to address key challenges in disaster management, including user allocation, processor allocation, energy consumption, and time delay factors. The QICSO algorithm is designed to optimize resource utilization by addressing key objectives such as minimizing energy consumption, reducing data processing delays, and improving the allocation rates of users and processors. A novel fitness function is formulated by considering these factors to ensure efficient disaster response. A novel representation of the quantum cuckoo and qubit has been developed, where quantum cuckoos are encoded and decoded using a novel hashing technique. The fitness function incorporates four objectives: user allocation rate, processor allocation rate, energy consumption, and delay. The proposed approach is simulated across multiple scenarios against the latest existing algorithms. Results demonstrate that the proposed algorithm significantly outperforms the compared methods. Additionally, statistical analyses, including analysis of variance (ANOVA) and the Friedman test, have been conducted. The Taguchi parametric statistical technique is employed to further evaluate performance. The simulation results indicate that QICSO surpassed existing approaches in terms of energy consumption by 18.3% and delay by 3.5%.</p>