Highway tunnels are susceptible to the influence of the geographical environment and other factors during the construction. The instability of the mountain structure may cause potential safety hazards, and tunnel subsidence is an important indicator of changes in the tunnel structure. Therefore, the model based on Bayesian optimized XGBoost is proposed to predict the tunnel subsidence. The complexity and serious interference of the tunnel construction scene hamper data collection and subsequent change analysis of subsidence. First, the time scale of the original subsidence monitoring data is unified. In accordance with time domain and spatial domain information, the outliers and missing values are repaired. Finally, the integrated Bayesian optimized XGBoost model is used to analyze the peripheral convergence, surface subsidence, and vault subsidence. Compared with the original XGBoost model and long short-term memory (LSTM) model, the Bayesian optimized XGBoost model has the highest accuracy. The average prediction accuracy of vault subsidence, surface subsidence, and peripheral convergence can reach 0.979 4. This model can effectively monitor and predict the change process of tunnel subsidence, which is of importance for practical application during the supervision of tunnel safety.