Unmanned Aerial Vehicles (UAVs) are transforming various fields, from surveillance to agriculture, necessitating advanced control systems for effective operation. This study explores three prominent control strategies for UAV trajectory tracking: Proportional-Integral-Derivative (PID) control, Sliding Mode Control (SMC), and Backstepping Control. Each method is enhanced through Gray Wolf Optimization (GWO), a nature-inspired algorithm designed to determine the optimal control gains, thereby maximizing performance in real-world scenarios. The paper begins by providing a thorough overview of each control technique, elucidating their underlying principles and typical applications in UAV systems. We employ GWO to fine-tune the parameters of each controller, enabling a systematic approach to optimization that takes advantage of the algorithm's ability to converge towards global optima. This optimization is critical, as the success of UAV operations heavily relies on precise trajectory tracking, which is inherently influenced by the selected control strategy. To evaluate the effectiveness of each approach, we conduct a series of simulations that track the UAV’s performance across various trajectories. Key performance indicators such as speed, precision, and robustness are meticulously analyzed. The results illustrate significant variances in how each controller performs under different operational conditions, with a detailed discussion of their respective advantages and limitations. For instance, while the PID controller is noted for its simplicity and ease of implementation, it may struggle with robustness in dynamic environments. In contrast, the Sliding Mode Controller exhibits superior resilience to disturbances, yet may require more complex tuning. The Backstepping Control method, on the other hand, demonstrates exceptional precision, particularly in complex maneuvers, but can be computationally intensive. This comparative analysis provides crucial insights for researchers and practitioners in the UAV domain, highlighting the importance of selecting the appropriate control strategy based on mission requirements. By integrating GWO into the optimization process, we pave the way for more efficient and reliable UAV control systems, ultimately contributing to the advancement of autonomous aerial operations. The findings underscore the potential for future work to explore hybrid control approaches that leverage the strengths of each method, further enhancing UAV capabilities in increasingly complex environments.