<p>The efficient deployment of IoT devices and real-time data processing in disaster-stricken areas presents significant challenges due to energy constraints, limited infrastructure, and the critical need for swift responses. Unmanned aerial vehicles (UAVs) with edge computing (ECs) emerge as a promising approach for data collection and processing in such scenarios. This paper introduces a novel hashing-based quantum-inspired particle swarm optimization (QiPSO) algorithm to tackle core challenges in disaster management, including computation unit allocation, delay, energy consumption, and price. The QiPSO algorithm is specifically designed to optimize resource utilization by focusing on key objectives: minimizing delay, energy consumption, and price, and enhancing IoT and computing unit allocation rate. The fitness function is designed to incorporate these objectives to ensure an effective disaster response framework. The proposed framework employs QiPSO to manage IoT device allocation for UAVs and ECs. A novel encoding and decoding strategy for quantum particles is introduced, leveraging an innovative hashing technique. The fitness function addresses four primary objectives: processor allocation rate, energy consumption, delay, and price. The algorithm’s performance is assessed through simulations across various scenarios against state-of-the-art evolutionary algorithms. Simulation results show that the QiPSO algorithm significantly outperforms competing methods. The evaluation includes statistical analyses such as ANOVA and the Friedman test, as well as the application of the Taguchi parametric statistical method to validate performance further.</p>