Axis deviation during shield tunneling may cause quality and safety problems, such as segment dislocation and cracking. Therefore, the shield attitude must be accurately predicted and effectively controlled. An intelligent prediction model and control method for shield attitude based on machine learning are proposed in this study. First, a shield attitude prediction model based on Bayesian optimization(BO) and support vector regression(SVR) is established using monitored construction data. The nonlinear relationship among construction parameters, stratum information, and shield attitude is revealed. Second, using the simulated annealing(SA) algorithm, the shield attitude control method for the dynamic adjustment of controllable construction parameters is proposed and applied in the engineering practice of the South Park stationSancha Street station section of the Binhai express in Fuzhou, China. Some conclusions are drawn: (1) After data preprocessing, feature screening, and BO hyperparameter optimization, the SVR-based shield attitude prediction model demonstrates excellent prediction performance and generalization ability. (2) It is important to set some optimization rules reasonably within the SA algorithm to ensure the operability of the recommended controllable construction parameters. (3) The application of the proposed control method in the case study shows that the vertical deviation of the shield tail is reduced from 45 mm to 18 mm during the successive 10-ring tunneling process, realizing a continuous and stable deviation correction.