A comprehensive understanding of TBM cutter wear is crucial for formulating effective boring plans and determining optimal intervals for cutter replacement. Cutter wear is a complex and non-linear phenomenon influenced by various factors. This study proposes a data-driven model for predicting TBM cutter wear values, incorporating TBM operational parameters, mechanical characteristics, and geological factors. Utilizing data from the Shenzhen Metro Chunfeng Road project, a dataset comprising ten parameters and 6445 data was curated, with the Transformer model employed as the predictive framework. The findings reveal that the Transformer model achieves a mean squared error (MSE) of 0.004, mean absolute error (MAE) of 0.0313, and correlation coefficient (R2) of 0.8544. Comparative analysis with the Long Short-Term Memory (LSTM) baseline model indicates the superior performance of the Transformer model across evaluation metrics. This suggests that the Transformer model exhibits enhanced capabilities in a data-driven context. The ability to predict cutter wear for the subsequent time interval holds significance for guiding construction activities.