The subsurface profile is usually mapped based on discrete borehole logs, geophysical investigation, and cross-borehole techniques. The geological profile is organized after making several assumptions and does not necessarily capture the ground's natural variability. Increasing the borehole density impacts the project's economics. The actual ground profile often varies from the predicted profile, leading to hazards during tunneling. In the case of a tunnel boring machine (TBM) excavated tunnel, probing could be deployed to understand the geometrical aspects that are about to be encountered at the tunnel face with the advancement. However, the probing data might not always represent the geometrical within the tunnel envelope. Researchers have proposed various machine learning (ML) based models in the last two decades to predict the ground geology in front of the TBM face. This paper attempts to review such models for different types of TBM, including earth pressure balance (EPB), slurry shield, hard rock, open-face, and dual mode. The first of this article summarizes the significant contributions published related to ML in TBM tunneling while critically evaluating the applicability of each mode. The second part of this proposal proposes a new supervised ML model that can be used in tunneling in different rock masses and applies to all the TBM types. TBM's continuously monitored parameters like face pressure, torque, revolutions per minute (rpm), advance rate, and total advance are considered inputs to the model. Such parameters, along with the extreme gradient boost (XGBoost) method, are utilized to understand the geotechnical characteristics of the material ahead of the tunnel face. The dataset will be used with input parameters and target variables as engineering properties of geological units, such as uniaxial compressive strength and porosity at and ahead of the tunnel face. The proposed model is expected to enhance the overall accuracy and interpretability of the predictions.