Artificial Intelligence Applications in Water Resources Management: Global Advances and Perspectives for Morocco

Abstract As water resources become scarcer, population growth, and climate change, complex water resources challenges have been turning to Artificial Intelligence (AI) as a promising solution. Water resources are becoming scarce and with the growing population and climate change, complex water resources challenges are turning to Artificial Intelligence (AI). This research seeks to analyze recent advancements in the use of AI and evaluate their feasibility for water management in Morocco. To conduct a systematic literature review, the PRISMA framework was used and searched through three databases: Scopus, Web of Science, and Google Scholar databases. Following this, 20 peer-reviewed and published studies between 2020 and 2025 were identified from an initial sample of 347 records based on pre-defined inclusion and exclusion criteria. The studies that were selected are the ones analyzed in this review and they fall into three application areas: hydrological prediction, smart water distribution, and water quality monitoring. The results show that the machine learning and deep learning models are much more effective at prediction accuracy, anomaly detection and real-time decision support than the traditional methods. Several issues remain, though, such as the lack of data, model interpretability and the high cost of implementation, especially in less developed countries. AI technologies have the potential to address forecast and prediction inaccuracies, mitigate water loss, and enhance water quality monitoring in the Moroccan context, but require investments in data infrastructure, human capacities, and regulations. In conclusion, the study suggests that, while there are significant technical and institutional hurdles to overcome, AI can play a role in enhancing water resources management to become more efficient and sustainable. Keywords: Artificial Intelligence, Water Resources Management, Machine Learning, Hydrological Prediction, Smart Water Systems.