## 4 Conclusions  
This paper is a first exploration of the significant influence of ML methods in the field of the water management aiming to facilitate the monitoring, analysis, and optimization of water resources use. In a world grappling with escalating water scarcity and increasing demand, the convergence of technological innovation and scientific knowledge offers a glimmer of hope. The utilization of ML has revolutionized the landscape of water resource management. From predicting water quality and detecting pollutants to mapping underground aquifers and monitoring distribution systems, these tools shape a more sustainable future for water. Furthermore, the fusion of supervised and unsupervised algorithms, supported by the analytical prowess of deep learning, propels us towards intelligent decision making. Although there are many advantages of such an extensive survey, there are also disadvantages/limitations which are associated with this review. In terms of advantages, this work consolidates information from various sources, highlighting trends, practices, and emerging techniques in water management using ML methodologies. It provides a knowledge synthesis, identifying gaps and issues for further investigation. It also intensifies interdisciplinary insights and approaches, serving as an educational resource for students, researchers, and practitioners interested in ML's application in water management. This foundational understanding makes it easier for newcomers to enter the field. Moreover, in our comprehensive exploration, we meticulously constructed tables that establish vital connections between diverse water management methodologies (distinct classification, regression, and other techniques). These tables offer invaluable insights into the practical applications of these methodologies and provide supporting references for verification, fostering a deeper understanding of the intricate field of water management. Overall, this extensive research covers the latest advancements in ML in connection to water management, aiming to maintain relevance despite potential outdatedness, balancing depth of analysis with breadth of coverage. ML methodologies offer promising solutions in water management, but they face challenges such as data quality and quantity, interpretability and explainability, generalization, and integration with domain knowledge. Incomplete or inaccurate data can lead to unreliable predictions, affecting decision making in water management. Deep learning models are often considered \"black boxes\", making understanding the reasoning behind model decisions crucial. Generalization is also a challenge, as ML models need to be able to make accurate predictions in new scenarios. Finally, integrating ML with domain-specific knowledge is essential for effectively addressing real-world water management problems. Addressing these limitations is vital to harness the full potential of ML in water management. Future research directions in ML in water resource management include the development of hybrid ML models that combine various algorithms to improve water resource management predictions. The goal is to enhance the interpretability of ML models for transparency and understanding for stakeholders. The research can be applied to integrated water-energy management, urban water systems resilience, data and sensor integration, data privacy and security solutions, cost-effective AI solutions, stakeholder education and engagement, sustainable water resource governance, and climate change adaptation. Hybrid ML models can improve the accuracy and robustness of water resource management predictions. Explainable AI in water management can enhance the decision-making process for stakeholders. Advanced data fusion techniques can improve real-time monitoring and decision making in water resource management. Data privacy and security solutions can be addressed by researching ML-based methods for securing sensitive data while ensuring accessibility for authorized stakeholders.