Economic development and population growth are putting considerable pressure on drinking water supplies, and the acceleration of climate change is making the situation more difficult. Given the complexity of water-related issues and the overabundance of data to be processed, researchers are increasingly turning to artificial intelligence to improve their understanding of phenomena and integrate this wealth of data as a lever for development. The present Scopus-based bibliometric study uses an incremental heuristic approach to explore the structure and trends of this research direction. We find that remote sensing, photometry, measurement of biological parameters, and physicochemical analysis of materials have emerged as strong trends for environmental and sustainability applications. The role of AI is essential due to the multitude of parameters to be analysed and the large quantities of information to be processed. Some AI concepts seem to be among the most common: ‘artificial neural networks’, ‘random forest’, ‘long-term memory’, ‘Short-Term Memory’, ‘Extreme Learning Machine’, ‘Fuzzy Inference System’, etc. The interest in observing and monitoring water quality, groundwater, evapotranspiration, and monitoring water levels seems to benefit agriculture and water resource management issues. Regarding international collaboration, there is a strong concentration on the most prolific countries (China, United States, India), remaining the main players in this field of research. Finally, the study revealed emerging topics of interest among the most dynamic Topics. This concerns, for example, the interactions of admixtures with water in technical mixtures, the dimensioning and calculation of stresses and compressions, and the dynamics of waves and large masses of water. This study thus offers indications and development prospects for research teams, particularly in countries on the margins of the current bibliometric distribution of the field. It is also very instructive to enrich these results with expert information, using other types of information (economic, ecological, etc.). This could improve the analysis consistency and the relevance of the findings.