Abstract: To address the problem of high-precision prediction of mine airflow in mine ventilation systems, an Improved Sparrow Search Algorithm (ISSA)-based BP neural network model (ISSA-BP) was developed. The ISSA, incorporating Cauchy mutation and opposition-based learning mechanisms, was used to globally optimize the initial weights and thresholds of the BP neural network, thereby reducing the interference of randomly initialized parameters with the training results and improving the convergence stability, optimization accuracy, and nonlinear fitting capability of the model when handling complex airflow time-series data. Comparative experimental results showed that the ISSA-BP model achieved a Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) of 0.75 m³/s, 6.2%, 0.98 m⁶/s², and 0.98 m³/s, respectively, with a coefficient of determination (R²) of 0.98, outperforming the Long Short-Term Memory (LSTM), BP, Particle Swarm Optimization (PSO)-BP, and SSA-BP models. The ablation experimental results showed that, after the opposition-based learning mechanism was introduced into the SSA-BP model, the MAE, MAPE, MSE, and RMSE decreased from 2.08 m³/s, 8.9%, 10.37 m⁶/s², and 3.22 m³/s to 1.96 m³/s, 8.2%, 9.85 m⁶/s², and 3.08 m³/s, respectively. After the Cauchy mutation mechanism was introduced, the corresponding metrics further decreased to 1.48 m³/s, 7.3%, 5.95 m⁶/s², and 2.44 m³/s, respectively. When the two mechanisms were integrated, the prediction errors were further reduced, indicating that their synergistic effect further improved the prediction accuracy and stability of the model.