<p>This work presents a resilient flight control system designed for modern airplanes operating under extreme aerodynamic conditions, particularly at high angles of attack where sensor faults and external disturbances pose significant risks to flight safety. The existing methods fail to simultaneously address uncertainties in nonlinear dynamics, external disturbances, and sensor faults, creating a critical gap that compromises the robustness and safety of flight control systems. The proposed framework consists of three integrated modules: (1) a modified adaptive super-twisting algorithm (MASTA) is designed incorporating novel element: the exponential reaching law technique, aimed at achieving precise angular rate estimation; (2) an adaptive neural observer fused with an extended Kalman filter (ANO-EKF) for real-time fault diagnosis and state reconstruction; and (3) a backstepping controller enhanced with a smooth sigmoid-based switching strategy for adaptive feedback control. MASTA autonomously calibrates observer gains without prior knowledge of disturbance bounds, improving resilience and estimation accuracy in nonlinear flight dynamics. ANO-EKF combines neural adaptability with stochastic filtering to ensure reliable fault accommodation in the presence of uncertainty. The backstepping controller facilitates smooth transitions between nominal and estimated feedback regimes, suppressing transient oscillations and minimizing control effort. Simulation results with a nonlinear representation of the F-18A jet demonstrate a 13.2% improvement in trajectory tracking and a 6.56% reduction in control chattering compared to traditional fault-tolerant methods. The transition from classical super-twisting strategies to the proposed adaptive MASTA design enhances estimation robustness and overall control integrity, with implications for reduced pilot workload and improved safety in dynamic operational environments.</p>