Path planning for maritime Unmanned Combat Aerial Vehicles (UCAVs) is critically influenced by dynamic wind conditions and sensor-dominated environments, where radar-based detection capabilities pose significant challenges to mission safety and operational efficiency. This study evaluates the performance of Particle Swarm Optimization (PSO) and Quantum-Inspired Particle Swarm Optimization (QPSO) in optimizing UCAV paths under four distinct wind scenarios, generated by rotating forecasted wind vectors. The threat environment incorporates a radar-based detection model governed by the inverse fourth-power law of distance, simulating realistic probabilistic threat landscapes. Sobol sequence-based initialization was employed to enhance search space exploration, ensuring robust algorithm performance. The results demonstrate that PSO consistently delivers superior path quality and stability across varying wind conditions, outperforming QPSO in the majority of test cases. Statistical analysis further confirms PSO’s dominance in producing optimal paths with reduced variability. These findings underscore the significant impact of wind and sensor-driven threats on path costs and highlight the necessity of integrating environmental factors into optimization strategies for reliable and efficient maritime UCAV navigation.