The chamber pressure is a critical parameter for ensuring construction safety and managing construction risks during shield tunneling. To address this, a method based on a residual neural network is proposed for predicting shield chamber pressure. Initially, tunneling parameter data from a specific section of the Nanjing metro are collected and analyzed to construct a residual neural network model comprising multiple residual blocks. The developed model is then utilized to predict shield chamber pressure and evaluate its performance. Subsequently, key model parameters, including the number of residual blocks, network width, and learning rate, are analyzed. The model′s prediction performance for chamber pressure under different parameter settings is compared to identify the optimal model structure. The results demonstrate that the proposed residual neural network model predicts chamber pressure with high accuracy, yielding predicted values closely aligned with actual measurements across different locations. Specifically, the determination coefficients for chamber pressures at positions #1, #2, #3, #4, #5, and #6 are 0.95, 0.96, 0.94, 0.90, 0.91, and 0.96, respectively, with root mean square errors ranging from 0.017 to 0.023 MPa. These findings indicate that the residual neural network model outperforms artificial neural network, support vector regression, and random forest models in accurately predicting chamber pressure.