Multi-UAV path planning refers to the problem of generating safe, collision-free, and efficient trajectories for multiple drones navigating a shared environment. As UAV systems become increasingly common in fields such as surveillance, remote sensing, gaming, and autonomous delivery, the need for robust multi-agent planning algorithms grows. Traditional planning methods struggle with obstacle-rich environments, dynamic conditions, and the additional complexity of drone-to-drone collision avoidance. PPSwarm, a hybrid algorithm that integrates Rapidly-Exploring Random Trees, Particle Swarm Optimization, and priority-based coordination, offers an approach that balances computational efficiency with reliable navigation in complex spaces. This paper explores the motivations behind multi-UAV planning, reviews foundational algorithms, and describes how PPSwarm combines existing techniques to improve performance in challenging scenarios.