This research investigates the optimization of airfoil design for fixed-wing drones, aiming to enhance aerodynamic efficiency and reduce drag. The research employs Kulfan CST and Bézier surface parameterization methods combined with global sensitivity analysis (GSA) and machine learning techniques to improve airfoil performance under various operational conditions. Particle swarm optimization (PSO) is utilized to optimize the airfoil design, minimizing drag in cruise and ascent conditions while ensuring lift at takeoff. Computational fluid dynamics (CFD) simulations, primarily using XFOIL, validate the aerodynamic performance of the optimized airfoils. This study also explores the generative design approach using a neural network trained on 10 million airfoil simulations to predict airfoil geometry based on desired performance criteria. The results show important improvements in drag reduction, especially during low-speed cruise and ascent phases, contributing to extended flight endurance and efficiency. These results can be used for small unmanned aerial vehicles (UAVs) in real-world applications to develop better-performance UAVs under mission-specific constraints.