Indoor environment construction for occupants has high energy consumption; as such, occupancy plays a noteworthy role in the complete life cycle phase of buildings, including design, operation, and retrofitting. In the past few years, building occupancy, which is considered the basis of occupant behavior, has attracted increasing attention from researchers. There are increasing requirements for buildings to be both comfortable and energy efficient; with the development of detection methods and analyzing algorithms, occupancy prediction has become a topic of interest for building automation and energy conservation. Therefore, this article reviews the literature regarding future building occupancy predictions (forecasting). This review is distinguished from occupancy simulation and detection research and focuses on the research purpose, physical routine, and complete methodology of occupancy forecasting. First, the research purposes, including the application field and detailed requirements for occupancy forecasting, are summarized and analyzed. Next, an overall methodology of occupancy forecasting, including data acquisition, modeling techniques, and evaluation, is discussed in terms of issues affecting prediction performance. Finally, the current challenges and perspectives of occupancy forecasting are highlighted, considering the insights of natural characteristics, on-site implementation, valid dataset sharing, and research techniques. Overall, accurate and robust future occupancy predictions will help to improve building system operations and energy conservation.