The present study utilizes multi-variate statistical techniques based on Geographic information systems (GIS) to evaluate groundwater quality status in the Anugul district of Odisha, India. Groundwater is a vital resource for drinking and agricultural purposes in the region, and its quality is critical for sustaining human health and agricultural productivity. The study employs various statistical methods, including Principal Component Analysis (PCA) and Geostatistical Analysis, to assess the spatial distribution and quality of groundwater parameters such as pH, total dissolved solids (TDS), electrical conductivity (EC), major ions, and trace elements. The data, collected from multiple groundwater sources, are integrated into a GIS platform to create spatial maps and identify the potential zone of contamination. The results reveal spatial heterogeneity in groundwater quality, highlighting areas of concern with elevated levels of specific contaminants. PCA identifies dominant factors influencing groundwater quality, while CA categorizes sampling sites into distinct quality clusters. Geostatistical analysis provides insights into spatial patterns and potential areas at higher risk. This research contributes to a better understanding of the groundwater quality dynamics in the Anugul district, enabling informed decision-making for sustainable water resource management. The findings in this study contribute, the good water type represents 60% of the groundwater samples identified in the study area. Hence 96% samples in the study area are fit for drinking. The GIS-based approach offers a powerful tool for policymakers, water resource authorities, and researchers to identify priority areas for intervention and formulate strategies to improve groundwater quality, ensuring the health and well-being of the local population and the sustainability of agriculture in the region.