This study aims to identify hidden patterns within seismic data collected during tunnel excavation using a mini-TBM in the laboratory. Seismic data in the form of acoustic emissions were recorded and analyzed, with features extracted from both time and frequency domains. Four distinct unsupervised machine learning techniques - namely, K-means, Density-based spatial clustering of applications with noise (DBSCAN), Gaussian mixture model (GMM), and Hierarchical clustering - were employed for analysis, and their outcomes were compared. The findings illustrate that while each algorithm presents advantages and drawbacks in uncovering patterns, Hierarchical clustering generally yields superior results compared to the others. Despite being computationally expensive, Hierarchical clustering offers notable advantages, such as robustness to outliers and noise, making it a reliable choice for clustering tasks, particularly in detecting hidden patterns within seismic data during TBM excavation. The methodology adopted in this research holds the potential for assessing the performance of TBMs in real-world tunnel excavation projects.