Machine learning (ML) is currently considered an effective tool in various scientific fields, including water resource management. Morocco is faced with climatic challenges, significant population growth and growing water requirements in the agricultural sector. In this context, several Moroccan researchers have been using machine learning models in various water-related fields. The aim of this study is to identify and analyze research trends, the main scientific contributions and advances in machine learning applications in the water sector in Morocco. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method to guide our search based on word queries in Scopus. Word queries included “machine learning”, “water” and specifying the Morocco affiliations. We selected 103 references and after word clouding, we found numerous topics, for example: quality; groundwater; management; prediction machine learning. In addition, the most recurrent machine learning models used by Moroccan researchers in this field are Random Forest (RF), Decision Tree (DT), Support vector machine (SVM). In this article, we present a comparative study of 5 available and most recent Moroccan scientific studies concerning: the ML models and parameters applied; the study area. The results show a significant increase in publications on the application of ML in hydrology in Morocco, particularly in recent years. The main areas of application include water quality, intelligent irrigation and the prediction of groundwater levels….