Neural networks possess strong function approximation capabilities and can be applied to Flush Air Data Sensing systems for air data estimation. Addressing the issues of random initial weights and thresholds, local optima during training, and high training data requirements inherent in traditional BP neural networks, an improved Particle Swarm Optimization algorithm-based neural network is proposed to enhance the prediction accuracy of the FADS system. The performance of this algorithm is validated through Computational Fluid Dynamics simulations, using pressure data from aircraft in both conventional flight and high angle of attack flight states. The results indicate that, under conditions of limited training data, the PSO-optimized neural network significantly improves air data prediction accuracy in both flight states. In conventional flight, the prediction errors for static pressure, Mach number, angle of attack, and sideslip angle are reduced by 54.88%, 60.46%, 53.76%, and 62.12%, respectively; while in the high angle of attack flight state, the prediction errors are reduced by 71.96%, 47.52%, 66.96%, and 53.41%, respectively. Furthermore, with the same data samples, the PSO-optimized neural network exhibits smaller error fluctuations across multiple training runs, demonstrating higher stability and reliability.