Trajectory tracking control is a critical aspect of quadrotor performance, requiring high precision, stability, and responsiveness. This paper presents a comparative analysis of PID control optimization for quadrotor trajectory tracking using Particle Swarm Optimization (PSO) and the Flower Pollination Algorithm (FPA). The study aims to evaluate the effectiveness of these metaheuristic algorithms for enhancing PID controller performance in terms of stability, precision, and response speed. The optimization process involves tuning the PID parameters using PSO and FPA to achieve optimal tracking performance. PSO, inspired by swarm intelligence, efficiently explores the search space to find optimal controller gains, whereas FPA, inspired by the pollination process in nature, provides a balance between exploration and exploitation. The optimized controllers are tested through simulations, where various trajectory scenarios are implemented to assess their performance. Simulation results indicate that both PSO and FPA significantly enhance the quadrotor’s tracking capability compared to conventional PID tuning methods. However, a comparative analysis reveals key differences in their performance. While PSO demonstrates faster convergence and improved response speed, FPA exhibits superior stability and precision under dynamic conditions. The results provide valuable insights into the suitability of these algorithms for real-world quadrotor applications. This study highlights the potential of bio-inspired optimization techniques to refine PID control strategies, offering a robust approach for the improvement of quadrotor trajectory tracking. These findings contribute to the ongoing development of intelligent control methods for aerial robotics and autonomous flight systems.