## 1 Introduction  
Water infrastructure integrity, quality, and distribution are pivotal for public health, environmental sustainability, economic development, climate change resilience, and social equity [1, 2, 3, 4]. Deterioration in water quality can propagate waterborne diseases, and ensuring access to uncontaminated and safe drinking water is imperative [5]. Safeguarding water infrastructure integrity and maintaining quality are vital for the efficient and responsible use of water resources for future generations [6]. Water plays a crucial role in agriculture [7], industry, and energy production [8], and meticulous oversight of water infrastructure can guarantee that these sectors receive the resources they need. The onset of climate change introduces alterations in both water availability and its quality [9], and steadfast practices in water infrastructure maintenance and quality assurance can assist communities in navigating these challenges [10]. Furthermore, water scarcities and degraded quality often bear a skewed impact on marginalized communities and economically challenged households [11]. Vigilant attention to water infrastructure integrity and quality ensures equitable access to pristine and consistent water resources for all. Machine learning (ML) can be used to improve water distribution and water quality by analyzing large amounts of data from sensors and other sources [12]. These algorithms can detect patterns and anomalies in water quality and distribution [13], allowing for the optimization of water treatment processes, reduction in water losses, and more efficient and effective water distribution. For example, machine learning can predict water demand based on weather patterns and other factors [14], helping utilities to adjust distribution accordingly. These algorithms can also detect leaks and other issues in the water distribution network [15], enabling utilities to identify and address problems before they become significant issues. In this study, an overview of ML applications in the realm of water infrastructure integrity and quality was conducted. Scopus was chosen as the primary database, and a search was initiated using the terms \"water management\" and \"machine learning\". The focus was placed on English-language articles published from 2015 onward, resulting in a total of 1087 articles. Following the initial phase, a decision was made to employ a natural language processing technique centered on topic modeling, as detailed in Section 2. By using this approach, the primary identification of the most pertinent topics was facilitated. It is crucial to highlight that in the second iteration of topic identification, the article selection was meticulously guided by the keywords that were extracted, the topic that was identified, and a review that was conducted by experts. Through this methodology, a detailed analysis of each topic was enabled. The goal of topic modeling is to cluster documents and words with similar meanings. This technique is commonly employed across various domains, such as natural language processing (NLP) and information retrieval (IR). Unsupervised ML algorithms are used to extract topics from collections of documents. Several topic modeling methods exist, including Probabilistic Latent Semantic Analysis (PLSA) [16], Latent Dirichlet Allocation (LDA) [17], and non-negative matrix factorization (NMF)--an unsupervised technique for dimensionality reduction in non-negative matrices [18]. NMF has been widely applied to discern underlying connections between texts and to discover latent themes [19], while these approaches do not necessitate labels for operation, they do require specifying the number of categories for clustering. Although numerous topic modeling systems are based on LDA and NMF, generating meaningful topics often entails significant effort in hyperparameter tuning. Generally, traditional methods exhibit certain drawbacks, such as their inability to capture semantic relationships between words when using bag-of-words representations.