<p>Wireless Sensor Networks (WSNs) are an important part of modern broadcasting systems because they enable the easy collection and transmission of data for numerous applications. Because sensor nodes (SNs) have imperfect energy possessions and network topology. Therefore, energetic, operative, and flexible routing approaches are essential. To advance energy competence and prolong network lifetime in WSNs, this study proposes the Fuzzy-based Swarm Intelligence Optimization Routing Technique (FSIORT). Fuzzy logic and swarm intelligence are combined in the FSIORT system to robustly adapt routing decisions based on numerous variables, such as network density, link quality, and node energy levels. By reproducing the cooperative behavior of biological systems such as ants and bees, swarm intelligence enables scalable, distributed optimization, while fuzzy logic provides a reliable approach for handling the inherent uncertainty in WSNs. The recommended method improves trade-offs among multiple parameters through adaptive learning. In terms of simulation, FSIORT outperforms predictable routing protocols across energy efficiency, packet delivery ratio, delay, network lifetime, throughput, and network steadiness time. Fuzzy-based decision-making reduces untimely node letdowns and maintains system connectivity over long periods by ensuring balanced energy consumption across nodes.</p>