There has been an increasing demand for underground construction due to urbanization and limited land in metropolitan cities in the recent years. However, the behavior of underground structures in soils and rocks is often not completely understood. The emergence of Artificial Intelligence (AI) techniques is envisaged to have a huge potential in addressing geotechnical problems that involve complex soil-structure interaction. This paper thus aims at reviewing the applications of AI techniques in studying underground soil-structure interaction, which focuses on aspects such as characterization of soils and rocks, pile foundations, deep excavations and tunneling. An overview of different AI techniques is provided and a list of key AI applications in underground works that have been published in the last ten years is also compiled to study the recent trend of machine learning techniques in underground construction. The capabilities and limitations of these techniques are discussed throughout the paper, to help readers understand various techniques that are suitable for different underground geotechnical applications. Lastly, some of the challenges that may be faced when applying the techniques are identified, and recent development of AI in geotechnical engineering is discussed in which possible countermeasures to overcome these limitations are highlighted.