## 1 Introduction  
The global political agenda has shifted towards urgently addressing environmental issues, particularly climate change, as they pose potential existential threats to humanity and civilization. As we face the destruction of Earth's ecosystem, it is crucial to explore innovative solutions to reverse damage and mitigate adverse effects caused by human activities [1]. The unsustainable depletion of natural resources, driven by population growth and increasing demands for food, water, and energy, is a pressing challenge. Traditional practices in the primary sector lead to overconsumption, environmental pollution, and land desertification. Poor water resource management, including inefficient irrigation and misuse, exacerbates the problem [1; 2]. Researchers and environmental governance stakeholders are utilizing emerging technologies like AI, ML, deep learning (DL), IoT, and wireless communications to address critical issues. These technologies enable automated production processes, optimize resource utilization, and minimize human intervention. High-resolution remote sensing techniques, smart information and communication technologies, and social media platforms have significantly improved data collection, analysis, and decision-making capabilities in weather forecasts, disaster management, and water-energy management [2]. At the same time, the exponential growth of data presents many challenges, making traditional statistical mathematical approaches inadequate. DL algorithms address these challenges by allowing crucial feature extraction and prioritization, optimizing decision-making processes, and reducing reliance on finite resources [2; 3]. AI applications have the potential to significantly improve water resource management, especially in urban areas. AI can enhance soil fertility, protect biodiversity, and optimize water management for sustainable agriculture [3]. AI has proven successful in fertilizer and crop selection, and smart irrigation systems using IoT sensors and ML techniques have contributed to freshwater conservation. Artificial neural networks can also optimize soil productivity and resource efficiency [4]. AI applications in water resources management include recording and forecasting water demand, infrastructure installation, water quality monitoring, and disaster prediction. These capabilities can help achieve the Sustainable Development Goals for Water (2030) by facilitating efficient data collection, analysis, and forecasting [5]. This paper utilizes the review papers, firstly, of the ML methodologies as a major research area of AI and computer science as a whole, and then the specialized review papers that explore the ML methodologies that have been applied to water management. The first set covers fundamental algorithms like decision trees, support vector machines, neural networks, clustering algorithms, and supervised classification algorithms within ML (logistic regression, k-nearest neighbor, support vector machines, etc.). The latter set of reviews is dedicated to the impact of a plethora of ML methodologies on analyzing climate change, water resource constraints, population growth, and natural hazards, which forces hydrologists worldwide to adapt and develop strategies to maintain water security. These reviews utilize the rapid growth of hydrologic data repositories and advanced machine learning (ML) models to provide improved assessments in hydrology. As we analyze in the specific subsections of this work, ML models contribute to optimal decision making in water resource management by efficiently modeling nonlinear, erratic, and stochastic behaviors. Furthermore, ML techniques can reduce computational costs, allowing decision makers to transition from physical-based models to ML models for complex problems.