Artificial intelligence and remote sensing for monitoring water quality in Sub-Saharan Africa

The increasing presence of contaminants in rivers, particularly those that are not regularly monitored, requires a proactive exploration of innovative approaches to improve water monitoring in aquatic ecosystems. Through the use of artificial intelligence (AI) and remote sensing technologies, new ways of monitoring and protecting water quality have been developed. The combined technologies enable organizations to make more thoughtful decisions about managing their aquatic environments because of their ability to offer real-time data at lower costs. The objective of this review paper is to examine how AI and remote sensing technologies enhance water quality monitoring and management systems. Recent research has shown that emerging techniques like generative adversarial networks further enhance these capabilities by improving data quality through augmentation and super-resolution. A scoping review of 60 studies published in 2012–2026 (Scopus, Web of Science and Google Scholar; PRISMA flow in Fig. 2) shows that research effort within Sub-Saharan Africa is highly concentrated: 30 studies (50%) were conducted in South Africa, eight countries contributed a single study each, and no study was found for Central Africa. Because the search was restricted to Sub-Saharan Africa, these counts describe the distribution of effort within the region; they do not quantify its share of global research. Water governance systems can be empowered to achieve intelligent and resilient solutions by implementing transparent AI systems along with multispectral Earth observation data and GIS integration methods.