Artificial intelligence technologies, especially Machine Learning (ML), have greatly improved the accuracy of data analysis for water quality assessment. Artificial intelligence and machine learning have significantly improved the assessment and prediction of water quality variables. These innovations effectively handle spectral data from several sources, identify pollutants, and assist in early detection systems. Artificial intelligence techniques require a broad dataset for maximum performance. Some research employed tiny datasets, reducing predictive potential. AI approach has the ability to cut costs, promote sustainable water treatment, and improve water and environmental quality. The study examined the benefits of deep learning models compared to traditional machine learning methods. Machine Learning models rely heavily on deep learning methods, making them essential for practical applications in water-based environments.