With sustainable development as their overarching goal, Urban Water System managers need to take into account all social, economic, technical and environmental facets related to their decisions. Decision support systems (DSS) have been used widely for handling such complexity in water treatment, having a high level of popularity as academic exercises, although little validation and few full-scale implementations have been reported in practice. The objective of this paper is to make a review of artificial intelligence methods (mainly machine learning) applied to UWSs and to investigate the integration of these methods into DSS. The results of the review show that artificial neural networks is the most popular method in the water and wastewater sectors followed by clustering. Bayesian networks and swarm intelligence/optimization have shown a spectacular increase in the water sector in the last 10 years, being the latest techniques to be incorporated but overtaking case-based reasoning. Whereas artificial intelligence applications to the water sector focus on modelling, optimization or data mining for knowledge generation, their encapsulation into functional DSS is not fully explored. We believe that the reason behind the misuse of Artificial intelligence methods in DSS is not related to the methods themselves but academic level and have not made it into practice probably due to the lack of an association between the fields of water engineering and computer engineering.