Thick-film resistors are widely used in electronic systems, where resistance accuracy and stability are critical for device performance. However, the screen-printing process involves complex multi-parameter coupling, and conventional optimization methods rely heavily on empirical adjustments, leading to low efficiency and limited precision. To address these issues, a data-driven process optimization method is proposed. It combines a genetic algorithm-optimized backpropagation (GA-BP) neural network with a chaotic particle swarm optimization (CPSO) algorithm. A resistance prediction model is constructed using production-line data to capture the nonlinear relationship between process parameters and resistance. The CPSO algorithm is further employed to achieve target-oriented inverse design of process parameters. The results show that the model achieves an R² of 0.991 and a mean absolute percentage error below 3.09% on the test set. The optimized parameters reduce the resistance error by up to 14.86%, with the deviations controlled within 5%. These results demonstrate that the proposed method improves the accuracy of process parameter optimization compared with the traditional experience-driven approach.