This paper presents a trajectory tracking control method for unmanned aerial vehicles (UAVs) using a sliding mode controller (SMC) integrated with a radial basis function neural network (RBFNN). The SMC ensures the stability and robustness of the system against model uncertainties and external disturbances, while the RBFNN approximates the unknown nonlinearities, reducing chattering compared to the conventional SMC. The integrated controller is developed for both position and attitude controllers, with Lyapunov-based analysis ensuring the stability of the system. Simulation results on MATLAB/Simulink demonstrate fast convergence, accurate tracking, and smooth control input under disturbance conditions, demonstrating the effectiveness of the proposed method. This study provides a practical solution suitable for real-time UAV deployment, offering potential for future applications in advanced UAV missions.