Maize (Zea mays L.) is a staple food crop that smallholder farmers mostly cultivate under rain-fed conditions in Southern Africa. Despite significant contributions to food production by smallholder farmers, they face climate change-related challenges such as drought, resulting in crop water stress and significant yield losses. This is exacerbated by the lack of financial resources, mechanical skills, and sound climate change adaptation strategies, increasing the yield gaps. This could potentially be addressed through technological advancements such as precision farming systems. Remote-sensing systems are sufficient and well equipped to address crop production’s complex and technical assessments, such as crop water stress, inexpensively and efficiently. This study sought to systematically review the literature on the progress, emerging gaps, and opportunities in applying remote sensing technologies in quantifying maize water stress. Adhering to the PRISMA guide, 100 peer-reviewed articles were examined from Web of Science, Scopus, Google Scholar, and ScienceDirect. Results significantly increasing research efforts have been exerted from 2002 to the present, with the majority of research articles (37%) being conducted in the United States and the least (12%) in the African continent. Specifically, 17 different Earth observation sensors were used to map maize water stress. Landsat is the most widely utilized sensor, particularly the red and near-infrared regions of the electromagnetic spectrum, along with their derivatives. These Landsat spectral derivatives are used mostly in conjunction with the surface energy model in retrieved literature. However, there is a dearth of literature on remote sensing maize crop water stress in smallholder croplands. This is mainly because these agricultural systems are extremely small (<1 ha) and heterogeneous to be detected by moderate spatial resolution sensors that are freely available. Furthermore, validation mechanisms, data, and fine spatial resolution suitable for these croplands are scanty, if not expensive. Providentially, UAV-based remote sensing technologies, which are relatively cheaper, with ultra-high spatial resolutions, and user-defined acquisition times have emerged as suitable alternatives. In this regard, more research efforts are required to assess the prospects of these technologies, especially in smallholder farms in southern Africa associated with limited resources.