Abstract: Agricultural greenhouses (AGHs) are increasingly used globally to control the crop growth environment, which are vital for food production, resource conservation, and rural economies. Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images (e.g., satellite and uncrewed aerial vehicle (UAV)). Research on this topic began in 1989, and the number of related studies has increased annually. This paper provides a review of the development of remote sensing of AGHs and research hotspots. It summarizes the current status and trends of data sources, identification features, methods, and accuracy of AGHs extraction. Due to the unique spectral, textural, and geometric characteristics of AGHs, research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more, such as Landsat, Sentinel, Gaofen (GF), and Worldview, to extract AGHs. Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods. In contrast, deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas, with accuracy rates generally exceeding 90.00%. However, future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction. Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs. Moreover, emphasis should be placed on optimizing data fusion techniques, implementing sample transfer methods, expanding the number of sensors, and increasing the application of artificial intelligence (AI) in monitoring AGHs. These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency. This review provides resources for researchers and decision-makers involved in modern agricultural development, as well as scientific evidence for the sustainable development of rural areas.