Accurately identifying the location and depth of buried utility assets became a considerable challenge in the construction industry, for which accidental strikes can cause important economic losses and safety concerns. While the collection of as-built utility locations is becoming more accurate, there still exists an important need to be capable of accurately detecting buried utilities in order to eliminate risks associated with digging. Current practices typically involve the use of trained agents to survey and detect underground utilities at locations of interest, which is a costly and time-consuming process. With advances in artificial intelligence (AI), an opportunity arose in conducting virtual sensing of buried utilities by combining robotics (e.g., drones), knowledge, and logic. This paper reviewed methods that are based on AI in mapping underground infrastructure. In particular, the use of AI in aerial and terrestrial mapping of utility assets was reviewed, followed by a summary of AI techniques used in fusing multi-source data in creating underground infrastructure maps. Key observations from the consolidated literature were that (1) when leveraging computer vision methods, automatic mapping techniques vastly focus on manholes localized from aerial imagery; (2) when applied to non-intrusive sensing, AI methods vastly focus on empowering ground-penetrating radar (GPR)-produced data; and (3) data fusion techniques to produce utility maps should be extended to any utility assets/types. Based on these observations, a universal utility mapping model was proposed, one that could enable mapping of underground utilities using limited information available in the form of different sources of data and knowledge.