<![CDATA[The exponential growth of environmental challenges, particularly those affecting water resources, necessitates innovative technological interventions beyond conventional approaches. This review explores the transformative potential of deep learning technologies in water quality management across different scales from controlled laboratory environments to complex oceanic systems. By analyzing recent developments, we identify how neural networks, especially convolutional and recurrent architectures, have revolutionized water quality parameter prediction, anomaly detection, and ecosystem monitoring. Integrating multi-modal data streams with advanced algorithms has enabled unprecedented predictive accuracy and real-time assessment capabilities, transforming reactive monitoring systems into proactive management frameworks. Despite significant progress, challenges remain in data standardization, model interpretability, and the practical deployment of these technologies in resource-constrained settings. This review critically assesses current research trajectories and identifies promising avenues for future development, emphasizing the importance of interdisciplinary collaboration in translating laboratory innovations to large-scale implementation for safeguarding our most precious resource]]>
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