This study investigates intelligent PID parameter tuning for wind-resistant flight control of quad-ducted fan drones. A machine learning framework integrates flight tests, computational fluid dynamics (CFD) simulations, and dynamic modeling to address strong coupling and nonlinear aerodynamic challenges. By establishing a dynamic response rate model and data-driven optimization framework, the research enables robust attitude control under varying wind disturbances, enhancing stability in hover, transition, high-speed forward flight, and wind disturbance rejection situations. Experimental validation demonstrates improved response rates and reduced sensitivity to gusts, providing a foundation for reliable low-altitude operations in challenging environments.