The national water strategy has been an essential vector of government strategy for a long time. The management of water resources is an integral part of the economic development of Morocco. Nevertheless, the definition of the strategic axes of this component and the adequate decision-making depends directly on the collection and use of all the data relating to water resources. If big data technologies present a suitable solution to ensure optimal and rapid use of its data, the success of functional and technical designs can only be provided after total control of the processing and decision-making processes relating to the water domain. In this paper, we will try to identify the aspects relating to the processes of data collection, processing, consolidation, and decision-making through the use of the results of field surveys and interviews with business managers.

This article will examine the data collection process, management flow, and decision-making process through interviews with public sector representatives responsible for water management.Natural resources have always been at the heart of the world’s most important government strategies. The development of economies is contingent upon the effective management of a country’s water resources, which in turn requires good water governance that depends on the collection of sufficient data and an effective data management system (1), especially with climate change continuing to impact this resource around the world.The management of natural resources, in general, necessitates collecting data and raw measurements, resulting in storing a large volume of data. Therefore, Big Data Analytics (BDA) technology is considered a primary means of processing such data. However, while Big Data technologies offer a suitable solution for ensuring optimal and rapid use of data, the success of functional and technical designs can only be guaranteed by maintaining complete control over the processing and decision-making processes.

Machine learning is emerging as a powerful tool across many scientific fields, including water resource management. In Morocco, growing challenges such as climate change, population growth, and high water demand—especially in agriculture—have led researchers to apply these techniques to water-related issues. This study reviews recent research conducted in Morocco, highlighting major trends, scientific contributions, and progress in machine learning applications for hydrological challenges. Following the PRISMA framework, a systematic search was carried out in the Scopus database, resulting in 103 relevant publications affiliated with Moroccan institutions. Using NVIVO and SPSS software, key themes were identified, including water quality, groundwater management, and groundwater level prediction. The most frequently used models include Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). This article presents a comparative analysis of nine highly cited Moroccan studies, focusing on application areas, models, parameters, and performance. Findings show a clear rise in AI-related hydrological research in Morocco, especially in water quality monitoring, smart irrigation optimization, and groundwater level forecasting.