In hypersonic flight control, characterized by challenges posed by input saturation, model parameter uncertainties, and external disturbances, this paper introduces a pioneering anti-input saturation control method based on RBFNN adaptivity. We have developed adaptive laws to enhance control system adaptability and robustness by integrating mission profiles, actuator saturation failure modes, and self-evolving neural network design. Furthermore, our approach introduces a novel anti-input saturation auxiliary system, effectively addressing input saturation constraints. This innovation ensures system stability and precise tracking, even in severe input saturation constraints. The results reveal that the system’s steady-state tracking error remains under 2% under input saturation constraints, and the convergence speed demonstrates an impressive 20% improvement. These findings underscore this research’s substantial advancement in hypersonic flight control. It may significantly enhance the controllability and performance of hypersonic vehicles in real-world scenarios.