This article addresses the critical issue of groundwater quality exploitation in urban areas and its repercussions on the peri-urban environment. Focused on Kolkata and its peri-urban surroundings within a 50 km radius of the city centre, the study aims to discern the intricate relationship between urban and peri-urban groundwater quality. The primary objectives include understanding the impact of urbanization on groundwater quality, identifying whether urban areas affect peri-urban regions or vice versa, and developing a machine learning-based model to predict groundwater quality for locations lacking in data. The study encompasses various physico-chemical parameters (pH, TDS, Ca, Na, Mg, K, Cl, HCO3, SO4, NO3, F, etc.) to comprehensively assess the groundwater quality. Methodologies employed that include descriptive statistical analysis for individual parameters, piper diagram creation for water type identification, spatial mapping of physiochemical parameters, Kohonen network and Self-Organizing Map creation for clustering pattern understanding, Entropy-based groundwater quality index formulation, and pollution level identification specific to groundwater. Furthermachine learning techniques, such as Artificial Neural Network for water quality analysis and Support Vector Machine for pollution level classification, are applied to achieve the study objectives. Results reveal that urbanization alone does not solely contribute to groundwater quality exploitation; natural factors, as well as industrial and agricultural activities, play pivotal roles in groundwater contamination. The study underscores the interconnectedness between urban and peri-urban areas in terms of groundwater quality, advocating a holistic approach. The proposed methodology is adaptable to other urban areas globally, providing a comprehensive framework for groundwater quality exploration.