Particle Swarm Optimization (PSO) has emerged as a powerful tool for addressing challenges in UAV technology, including path planning and network optimization. Despite its demonstrated success in improving convergence speed, path quality, and network performance, current research often needs to explore these areas, focusing on either pathfinding or network optimization without exploring their integration. This research addresses this issue by creating a multi-objective PSO-based framework that optimizes UAV path planning and network performance in dynamic and complex environments. By leveraging hybrid PSO algorithms, this research explores the potential for enhanced scalability, real-time adaptability, and multi-drone coordination in urban navigation and emergency response operations scenarios. Initial simulations indicate promising improvements in path efficiency and network stability compared to conventional methods. The findings of this study contribute to a more holistic understanding of how PSO can be applied to UAV systems, offering practical insights for future applications in diverse sectors such as agriculture, disaster management, and telecommunications.