Materials are continuously accumulating in the human-built environment since massive amounts of materials are required for building, developing, and maintaining cities. At the end of their life cycles, these materials are considered valuable sources of secondary materials. The increasing construction and demolition waste released from aging stock each year make up the heaviest, most voluminous waste outflow, presenting challenges and opportunities. These material stocks should be utilized and exploited since the reuse and recycling of construction materials would positively impact the natural environment and resource efficiency, leading to sustainable cities within a grander scheme of a circular economy. The exploitation of material stock is known as urban mining. In order to make these materials accessible for future mining, material quantities need to be estimated and extrapolated to regional levels. This demanding task requires a vast knowledge of the existing building stock, which can only be obtained through labor-intensive, time-consuming methodologies or new technologies, such as building information modeling (BIM), geographic information systems (GISs), artificial intelligence (AI), and machine learning. This review paper gives a general overview of the literature body and tracks the evolution of this research field.