This study explores the integration of artificial intelligence (AI) into aerodynamic optimization processes for Unmanned Aerial Vehicle (UAV) prototypes in subsonic wind tunnel environments. Traditional aerodynamic testing, while reliable, often demands extensive manual parameter adjustments and prolonged experimental cycles. By incorporating AI-driven computational models, machine learning algorithms, and real-time data analytics, we demonstrate a more efficient approach to shape refinement, drag reduction, and stability enhancement. Our results show that AI-based optimization reduces testing time by up to 35% while improving lift-to-drag ratios and aerodynamic stability. The findings underscore the potential of AI to transform UAV design cycles, reduce costs, and accelerate the deployment of advanced aerial systems.