Variations in flight parameters and inherent uncertainties in aerodynamic systems have a profound effect on the performance and stability of missile autopilot systems. The main objective of this research is to improve the performance of the autopilot under parametric uncertainties and varying flight conditions. To achieve this, a Multiple-Model Adaptive Control (MMAC) approach is proposed, whose main innovation lies in the simultaneous integration of switching and adaptive adjustment mechanisms within an model bank. This method, while maintaining system stability, allows for rapid selection of a model close to the actual behavior of the system, and by updating control parameters in real time, it provides faster response, less error, and higher stability than classical adaptive methods. This structure enables the controller to automatically select the most appropriate model by continuously comparing the model responses with the actual system behavior and retuning the control parameters accordingly. As a result, system stability against rapid parameter variations and nonlinear flight dynamics is significantly enhanced, while maintaining adaptability over a wide range of operational conditions. For validation, the proposed control method is compared with the Model Reference Adaptive Control (MRAC) strategy in a MATLAB/Simulink environment. Simulation results indicate that the proposed MMAC reduces settling time by approximately 70%, minimizes the steady-state error to nearly zero, and decreases the RMSE value by over 50% compared to MRAC. These improvements clearly demonstrate the superiority of the proposed design in terms of convergence speed, robustness, and tracking accuracy, particularly under rapidly changing or highly maneuvering flight conditions.