Tunnelling in urban areas should be aware of deviations from the design tunnel axis, referred to also as ‘misalignment’. Significant misalignment can cause unfavourable soil deformations, threatening adjacent properties, and sustainable development of underground space. In this study, the potential of applying machine learning (ML) and deep learning (DL) algorithms to EPB shield posture prediction and anomaly detection was explored. A framework, including data pre-processing, development of prediction models, performance evaluation, and anomaly detection, was proposed to tackle the said issue. The principal component analysis (PCA) was introduced not only to eliminate outliers but also to isolate salient data features using the Pearson's correlation coefficient (PCC). Also, the partial dependence analysis aimed to marginalize other features and established the relationship between an individual tunnelling parameter and EPB shield position. Then the feature-based subseries were normalized for training and examining the DL-based Long Short-Term Memory (LSTM) and ML-based ensemble EPB shield position predictors. On the other hand, three anomaly detectors founded on three ML algorithms were developed to prevent snakelike advance. Results showed that the DL-based LSTM predictor outperformed the ML-based ensemble predictor owing to its relative insensitivity to lags of unknown duration between important events. The thrust force dominated EPB shield position. The LSTM model prevented the accuracy of EPB shield position prediction from disturbing by gravels surrounding the design tunnel axis or artificial intervention.