In nature, birds can intelligently adapt their wing shapes to their environment. This paper aims to replicate this capability by designing an online data-driven aerodynamic performance optimization framework for an unconventional morphing aircraft. Compared to state-of-the-art methods, the proposed framework can more efficiently search for optima with reduced computational load when addressing time-varying and nonlinear problems. It also demonstrates enhanced adaptability to unforeseen scenarios. In the event of a sudden actuator fault, the algorithm can automatically detect the fault, adapt the onboard data-driven model, and continue performing optimization and trimming tasks using the remaining healthy actuators. Additionally, the paper addresses the optimal number of actuators within a morphing surface, considering the tradeoff between aerodynamic optimization performance and weight penalty. High-fidelity simulations on a flying-wing aircraft platform demonstrate that through active morphing, the proposed framework achieves drag reductions of 1.9–4.9% during cruise and up to 12.6% at higher operational lift coefficients (due to heavier weight and lower speed), resulting in an overall drag reduction of 2.97% over a typical flight cycle, which corresponds to fuel savings of approximately 188.97 kg∕h. This research represents a significant advancement in sustainable aviation, contributing to reduced fuel consumption, lower emissions, and improved fault tolerance for next-generation aircraft.