The paradigm of WSNs acted as a powerful source of data in real time for disaster management to make better and punctual decisions. In the proposed paper, an effort has been carried out to present a hybrid approach combining Genetic Algorithm, Artificial Neural Networks, and Particle Swarm Optimization: Investigating the Performance of WSNs and the Energy Efficiency in the Time of Tragedy. The design here will focus on optimal routing paths that will be energy-aware, with respects to robust network performance metrics such as latency, delivery ratio, drop rates, throughput, energy usage, node lifetime, overhead, and overall system efficiency. The proposed framework is evaluated by simulations in the NS2 simulator, comparing its performance against traditional PSO and GA models. Thus, the results indicate the hybrid approach greatly improves network efficiency as well as reliability. It thus proves to be an efficient approach for energy-efficient deployments of WSN in applications related to disaster management.