Abstract: To address the difficulty in simultaneously improving the prediction accuracy and robustness of existing coal mine gas concentration prediction models, which limits the reliability of gas safety monitoring and early warning, a hybrid prediction model integrating variational mode decomposition (VMD), a convolutional neural network (CNN), and a bidirectional long short-term memory network (BiLSTM), termed VMD-CNN-BiLSTM, was proposed. First, VMD was employed to decompose and reconstruct the original gas concentration time-series data, thereby reducing the adverse effects of data nonstationarity, noise interference, and mode mixing on prediction modeling. Subsequently, a coupled CNN-BiLSTM network was constructed. The CNN was used to extract local fluctuation characteristics and multiscale features from each modal component, whereas the BiLSTM was employed to capture bidirectional long-term temporal dependencies in the gas concentration time-series data. The extracted features were then fused to generate the final prediction results. Experimental validation was conducted using gas concentration monitoring data collected from a working face in the Pingdingshan mining area, and the proposed model was compared with the CNN, long short-term memory network (LSTM), BiLSTM, CNN-LSTM, and CNN-BiLSTM models. The results demonstrated that the VMD-CNN-BiLSTM model achieved the best prediction performance, with a coefficient of determination (R²) of 98.1597%, a mean squared error (MSE) of 0.00069499, and a mean absolute error (MAE) of 0.019251. Compared with the benchmark models, the proposed model increased R² by 3.7540 to 32.4854 percentage points, while reducing MSE by 0.0014 to 0.0128 and MAE by 0.0121 to 0.0667. These results indicate that integrating VMD with CNN-BiLSTM can effectively enhance the feature representation and temporal modeling capabilities for nonstationary gas concentration time-series data. The proposed model can therefore provide technical support for advance gas concentration prediction, safety monitoring, and early warning in coal mines.