<p>Accurately identifying and predicting long-term deformation of tunnel surrounding rock is crucial for the stability assessment and safety control of underground engineering. However, conventional prediction approaches are often limited by idealized assumptions and single modeling frameworks, which hinders their ability to achieve high-precision forecasts. Utilizing over 8 years of deformation monitoring data collected from a drill-and-blast-excavated experimental tunnel at the Beishan exploration tunnel test platform, this study proposes a novel hybrid forecasting model ALA–BiTG by integrating the Artificial Lemming Algorithm (ALA), Bidirectional Temporal Convolutional Network (BiTCN), and Bidirectional Gated Recurrent Unit (BiGRU). In the proposed model, the BiTCN captures long-range temporal dependencies and bidirectional patterns in time-series data, the BiGRU enhances bidirectional temporal dynamics, and the ALA optimizes the hyperparameter configuration of the BiTG subnetwork. The results demonstrate that the proposed ALA–BiTG model significantly outperforms benchmark models including BiTG, CNN-LSTM-ATT, BiGRU, and LSTM on the testing set, achieving a Coefficient of Determination (<i>R</i>²) of 0.971 and a Root Mean Square Error (RMSE) of 0.0016. Furthermore, comparative experiments with several mainstream metaheuristic optimization algorithms reveal that ALA achieves superior performance in convergence speed, computational efficiency, and optimization accuracy, demonstrating its effectiveness and applicability in complex engineering time-series modeling. Finally, based on existing time-series data, reliable prediction of the deformation pattern of surrounding rock in the next 1000&#xa0;days can be achieved.</p>