This study aims to examine Moth-Flame Optimization (MFO) algorithm by proposing the use in improving the UWSNs system. UWSNs are indispensable for numerous underwater applications; nevertheless, they are confronted with several crucial hindrances, for example, bandwidth constraint, high-latency communication, and limited energy. Since these are complex problems, optimizing them using various techniques presents a big challenge that could be addressed by bio-inspired optimization algorithms such as the MFO. The motivation for the development of MFO, which emulates the flight pattern moths for navigation, is to aid in the determination of optimal node positioning, routing path, and energy usage in the case of UWSNs. MATLAB workflow includes data collection, algorithm customization, and simulation and forms the basis of the overall methodology applied in the study. A comparison is made with regards to parameters such as network lifetime, energy utilization, data transmission success rate and delay using criteria such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performance comparisons made between the proposed MFO algorithm and the traditional GA and PSO algorithms prove MFO to be highly effective attaining higher values for all the considered parameters. Thereby, the results demonstrate that the use of MFO has significant promise for increasing the UWSNs lifetime and reliability, making the technique more suitable for underwater surveillance and investigation. It is important for interested researchers and practitioners to understand how some of these bio-inspired optimization techniques can be employed in complex scenarios such as UWSNs, and how this research can inform future work in this area. © 2025 Elsevier B.V., All rights reserved.