This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain of water resource management. Environmental issues, such as climate change and ecosystem destruction, pose significant threats to humanity and the planet. Addressing these challenges necessitates sustainable resource management and increased efficiency. Artificial intelligence (AI) and ML technologies present promising solutions in this regard. By harnessing AI and ML, we can collect and analyze vast amounts of data from diverse sources, such as remote sensing, smart sensors, and social media. This enables real-time monitoring and decision making in water resource management. AI applications, including irrigation optimization, water quality monitoring, flood forecasting, and water demand forecasting, enhance agricultural practices, water distribution models, and decision making in desalination plants. Furthermore, AI facilitates data integration, supports decision-making processes, and enhances overall water management sustainability. However, the wider adoption of AI in water resource management faces challenges, such as data heterogeneity, stakeholder education, and high costs. To provide an overview of ML applications in water resource management, this research focuses on core fundamentals, major applications (prediction, clustering, and reinforcement learning), and ongoing issues to offer new insights. More specifically, after the in-depth illustration of the ML algorithmic taxonomy, we provide a comparative mapping of all ML methodologies to specific water management tasks. At the same time, we include a tabulation of such research works along with some concrete, yet compact, descriptions of their objectives at hand. By leveraging ML tools, we can develop sustainable water resource management plans and address the world’s water supply concerns effectively.

This paper aims to explore trends in the application of big data and Machine Learning (ML) in Water Resources Management (WRM) by categorizing research studies into distinct scientific subfields. A comprehensive analysis was performed on articles published between 2018 and 2024. Leveraging a dataset of 6,430 collected papers, 173 articles were evaluated using bibliometric techniques to track the development of academic interest and recognize pivotal studies. Our suggested unsupervised classification model established categories and organized relevant articles according to their specific scientific focus, using keywords extracted from titles, abstracts, and author-defined keywords, with stop-words excluded. The model achieved a validation accuracy of 90.16% through the Multinomial Naïve Bayesian (MNB), 86.54% Random Forest (RF) and 84.61% Support Vector Machine (SVM) approaches. The analysis revealed 10 distinct research topics, emphasizing WRM and innovative city applications as leading categories. This study contributes to the literature by introducing a methodology for analyzing existing research, uncovering emerging scientific areas within WRM, and highlighting potential avenues for future investigation.