Water quality contamination jeopardizes watersheds by harming aquatic organism, disrupting food webs, and reducing biodiversity. Without appropriate and trustworthy information on the water qualities and types of contamination, over a million people who live in the Baitarani River basin area significantly rely on the river water for residential, drinking, irrigation, and industrial uses. To fulfil the objectives of sustainable development, maintaining continuous surface water quality monitoring and management is essential. Despite its significance, there are currently no specific tools available for assessing parameter contamination in surface water. Addressing this gap, the research objectives present a novel method for determining the appropriate geographical patterns of human consumption and potential risk areas for conflicts involving human water use. Further, the drinking water suitability and water vulnerable zones were determined on geographical and temporal scales, by utilizing Simple Aggregation of Preferences Expressed by Ordinal Vectors-Multi-Decision Makers (SAPEVO-M) Analysis, Geographical Information System (GIS) integrating MCDM analysis. In this study, a total of 22 water samples were collected from thirteen locations during the 13-year (2010–2023) monsoon period and then analyzed for their physicochemical parameters. The levels of physiochemical parameters like Turbidity, FC and TC found in the investigation region exceeded the permissible reference value in water samples. Hence, its concentration above the threshold limit can be directly linked to the food chain through plant uptake. These high contamination of parameters poses an ecological risk to the river, leading to human risks in these regions. From the results, it is noticed that Ca2+ is the most dominant cation with HCO3− as the dominant anion. Geospatial approaches such as Inverted Distance Weighted (IDW) are employed to interpolate the spatial variability of point attributes and predict an unobserved location using nearby known attributes and it is represented in the form of maps using ArcGIS software. Using geospatial analysis, the SAPEVO-M-based water quality index (X-WQI) was calculated. The results show that 30.77% (n = 4 samples) comes under excellent and good (38.46%, n = 5 samples) surface water categories. In addition, WQI involves contradictory difficulties even though it is a crucial component for grading underutilized water stations. Consequently, Multiple-criteria decision making (MCDM) models, such as Combined Compromise Solution (COCOSO) operators employing the WQI index were used to reduce contradictions. Based on this, the factor weights and field data were taken into account to determine the final ranking. The proposed approaches depicted site X-(8) was the most polluted in comparison with other locations. Overall findings revealed that domestic wastewater, illegally dumped municipal solid waste and agricultural runoff were the leading sources causing adulteration of the river’s water quality. The current analysis comes to the conclusion that in order to ensure the resource's sustainable usage, stringent management procedures must be put in place. We cannot utilize the surface water at X-(8), (11), (12), and (13) directly without treatment. Furthermore, by offering new perspectives on the literature, the study's findings support academics and practitioners in implementing cutting-edge MCDM utility approaches like COCOSO in many application domains. Future management and risk assessment of watersheds should take these findings into account. Thus, this study is the first effort to create WQI methodologies that take into account all concentrations of heavy metals, including water quality indicators that are often checked.