<p>Water is the most important requirement for any living thing's survival and nourishment. Sustainable surface water development stands out as a contemporary problem for rising worldwide populations, particularly in pressured riverine arid and semi-arid regions. The inhabitants of the Mahanadi Basin settlement depend only on surface water for household and agricultural needs due to groundwater scarcity and contamination. In this current investigation, sixteen water quality parameters and a total of nineteen surface water testing locations were collected and used as the dataset. The research spans a three-year (2021–2024) evaluation of water quality. However, resource management and environmental sustainability are greatly impacted by the rapid changes in land use and urbanization, especially in developing nations. Faster and cheaper control is required due to the real-world impact of low water quality.&#xa0;With this motivation, the present study therefore intends to examine the surface water’s physicochemical and geochemical composition for domestic and agricultural suitability using integrated approaches such as the geographical information system (GIS), drinking water quality index (D-WQI), entropy (E/X)-WQI, stepwise weight assessment ratio analysis (SWARA/I)-WQI, WASPAS (weighted aggregated sum product assessment)—TOPSIS (technique for order of preference by similarity to ideal solution), symbolized as (WT), and machine learning (ML) techniques such as random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models. Additionally, this study examines the spatiotemporal dynamics of land use land cover (LULC) from 2021 and projects trends to 2024. The pH of water samples was slightly alkaline at all locations. The concentration of cations and anions in water was in the order of Ca²⁺ &gt; Mg²⁺ &gt; Na⁺ &gt; K⁺, and Cl⁻ &gt; SO₄²⁻ &gt; NO₃⁻ &gt; F⁻, respectively. However, challenges such as elevated TC, EC, and hardness in water samples that exceed the permissible limit across the testing period necessitate targeted interventions. The D-WQI was found to vary between 23.70 and 96.09. The results indicated that 68.42 and 15.79% of water locations fall in the excellent to good class, thereby making them appropriate for irrigation as well as household use. The entropy index result showed that saltwater intrusion had a significant impact on eleven sample locations. The detailed analysis of E-WQI revealed that 42% of samples were fit for drinking purposes. Regarding the overall suitability of surface water for drinking and domestic use, the I-WQI values revealed that 36.84 and 26.32% were found to have poor water quality and thus are unsuitable for human consumption. Localized quality hotspots were identified using weighted overlay analysis in GIS, revealing locations such as U-(9), (2), (19), and (8) that were substantially degraded, as shown by the WT method.&#xa0;This supports reports of uncontrolled industrial effluent discharges that pollute water. Three machine learning algorithms, which include SVM, RF, and XGBoost, were combined and verified to forecast the WQI, which was derived from the SWARA technique. The findings showed that the best prediction performances were obtained when XGBoost was combined with random search. The study's methods for surface water quality, which use hydro-chemical characteristics as input variables, have been shown to be both practical and affordable. They are also very helpful for planning and managing water resources. According to the LULC data, rangeland (− 4.62%) and constructed area (− 0.84%) have seen negative changes, while trees (+ 3.91%), water (+ 0.21%), crops (+ 0.77%), and bare ground (+ 0.14%) have seen positive changes between 2021 and 2024. Overall, the results of the study show that the river water is safe to drink in the majority of the places. Therefore, it is advised that water quality testing be performed at least every three years to reduce contamination, potential health risks, and assess irrigation suitability. These findings help advance our knowledge of forecasting water quality indicators, assisting with efficient management of water resources, and aiding in efforts to adapt to climate change.&#xa0;Through strategic interventions that include treatment facilities, drainage infrastructure upgrades, and pollutant discharge laws, this framework gives utilities and urban planners the essential information they need to create targeted surface water quality restoration policies.</p> Graphical Abstract <p></p>