Shield tunneling construction is a predominant method in metro infrastructure development, where the wear of tunnel boring machine (TBM) cutters is a critical issue that slows down its construction. The factors affecting cutter wear are multifaceted and complex. Traditional empirical formulas tend to oversimplify, being limited to specific engineering contexts and exhibiting low generalizability. Conventional cutter replacement is immensely time-consuming and labor-intensive. This study explored the application of machine learning techniques to predict cutter wear, utilizing six different models: Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Convolutional Neural Network (CNN). A comparative analysis is established to evaluate the suitability of these algorithms for this task. Data sourced from the 15.2m diameter Chunfeng Road Tunnel project in Shenzhen, China, was enriched by proportionally distributing cumulative wear across each tunnel ring, followed by outlier removal using boxplot methodology and normalization of the data. Hyper-parameter optimization, including grid search techniques, is applied to model optimization. The outcomes revealed the 1D-CNN model’s superior ability to predict cutter wear trends, yielding a test set R2 of 0.872 and MAPE of 0.077, outperforming other machine learning models. Additionally, in cumulative cutter opening sections, the 1D-CNN model only has three sections with error percentages exceeding the 30% margin, satisfying the accuracy requirements. Extensive experimental investigations revealed that the 1D-CNN is preferentially selected for this cutter wear prediction task, providing empirical insights beneficial for cutter replacement decisions in construction.