Water pipes deterioration modeling has been a prevalent research topic in the last two decades due to high water break incidents and contamination rates. Failure processes are de facto very intricate to be diagnosed since there is a time lag between the failure incidence and consequences. Artificial intelligence (A.I.) techniques have gained much momentum during the last two decades, specifically for the deterioration modeling and assessment of water distribution networks. However, a comprehensive critical review on water infrastructure modeling via artificial intelligence and machine learning techniques is missing in the literature. This paper aims to bridge the gap in the body of knowledge and address the aforementioned limitations. The intellectual contributions of this paper are twofold. First, a comprehensive literature review method is presented through sequential steps that systematize and synthesize the literature in a scientific way. The state-of-the-art of AI-based deterioration modeling for urban water systems is revealed along with models' methodologies, contributions, drawbacks, comparisons, and critiques. Second, future research directions and challenges are recommended to assist the construction automation research community in setting a vibrant agenda for the upcoming years.