Abstract: Existing control methods for negative pressure of gas extraction boreholes exhibit delayed responses to changing operating conditions and lack adaptive capability and dynamic feedback control, making it difficult to achieve precise control of borehole negative pressure. To address these problems, an intelligent control method for negative pressure in gas extraction boreholes based on Particle Swarm Optimization (PSO) and Back Propagation (BP) was proposed. A coal seam gas–air migration model was derived, and on this basis, COMSOL numerical simulation software was used to obtain gas extraction datasets under different extraction conditions. A PSO algorithm was introduced to optimize the initial weights of the BP algorithm, improving the reliability of negative pressure prediction for gas extraction. Taking gas extraction flow rate or gas extraction volume fraction as the target value, the PSO-BP algorithm predicted the corresponding extraction negative pressure, and the valve opening was adjusted to make the borehole negative pressure reach the predicted value, thereby achieving precise control of gas extraction boreholes. The results showed that, compared with Extreme Learning Machine (ELM), Temporal Convolutional Network (TCN), and Support Vector Machine (SVM) algorithms, the BP algorithm more accurately captured the variation patterns of gas extraction data characteristics. The PSO-BP algorithm achieved better performance than the BP algorithm in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²). After on-site implementation of intelligent borehole negative pressure control, both gas extraction volume fraction and gas extraction flow rate increased compared with those before implementation.