Groundwater stands as one of the most crucial and renewable resources for all life on Earth. Assessing the water’s quality is crucial to preserving the ecosystem’s balance and longevity. The general quality of water has a significant effect on environmental preservation as well as human health. Water is used for domestic, agricultural, and industrial uses, among others. The Water Quality Index is an essential indicator for evaluating the success of water management. An assessment of water’s biological, physical, and physiological qualities establishes whether or not it is suitable for a given use. Water quality analysis has become a critical issue in today’s globe due to industrialization, agricultural activities, and human behavior. Real-time monitoring was outdated since traditional techniques of assessing water quality required costly testing facilities and numerical procedures. Because of the poor quality of groundwater, a more practical and economical solution is required. Techniques for categorization based on machine learning exhibit potential for quick assessment and identification of water quality. Machine learning algorithms have proven to be an excellent means of predicting the quality of water. The results of this research should improve machine learning applications for improving groundwater quality and groundwater development planning. The analysis highlights the effective use of models like deep learning, ensemble approaches, neural networks, support vector machines, and linear regression to predict groundwater quality, identify contamination sources, and optimize remediation techniques. It also shows how versatile machine learning techniques are in this regard. The study highlights the critical significance that high-quality and readily available data have in the success of models.