Abstract: To address the challenge of low computational efficiency in the numerical simulation of aerodynamic characteristics of airfoils equipped with Gurney flaps, this study proposes a novel hybrid prediction method that integrates proper orthogonal decomposition (POD) with neural networks. Traditional aerodynamic prediction methods based solely on computational fluid dynamics (CFD) or single machine learning models often struggle with high computational costs and limited ability to capture detailed local flow features, especially under varying flow conditions. The proposed approach first applies the POD method to extract the leading i principal modes from high-fidelity pressure distribution data, thereby constructing a compact low-dimensional feature space that encapsulates the dominant flow structures around the airfoil. Each sample is then reconstructed using the corresponding modal coefficients, and multiple neural networks are independently trained to learn the nonlinear mapping relationships between flow conditions and these coefficients. This multi-model framework enables the prediction of pressure distributions with both high resolution and improved generalization across different flow regimes. The RAE2822 airfoil with an installed Gurney flap is selected as the test case, and pressure distribution datasets under multiple flow conditions are generated through high-precision CFD simulations. Validation against experimental data shows a maximum deviation of less than 6%, demonstrating the accuracy of the simulation process. The experimental results further indicate that the proposed hybrid method achieves a prediction accuracy of 91.11%, significantly outperforming traditional algorithms such as random forest by a margin of 18.83%. In addition to improved accuracy, the method also achieves notable gains in computational efficiency due to the reduced dimensionality and parallel training architecture. Overall, this research not only enhances the prediction performance for airfoils with Gurney flaps but also provides a generalized and scalable framework for accelerating aerodynamic simulations involving complex geometries and flow conditions. It offers valuable insights into integrating data-driven techniques with physics-based methods for advanced aerodynamic analysis and design optimization.