<p>Water quality and quantity affect crop productivity, with surface water quality having a significant impact. The amount of surface water being used for drinking is gradually rising. Thus, assessing surface water quality and related hydro-chemical characteristics is essential for surface water resource management in Mahanadi River Basin, Odisha. The current study examined surface water quality and appropriateness for drinking and agriculture, utilizing several techniques such as Weighted Arithmetic (WA) Water Quality Index (WQI), Multivariate models namely Pearson Correlation, Cluster Analysis (CA) and Principal Component Analysis (PCA), six multiple machine learning (ML) techniques like, gaussian process regression (GPR), linear regression (Stepwise), fit binary tree (FBT), support vector regression, SVM (linear and polynomial kernels), and artificial neural network (ANN) to predict the WQI, for sustainable use of the surface water resources. Thirteen physicochemical parameters were used to analyse eleven surface water samples, which indicating that the primary cation and anion concentrations were as follows: Mg²⁺  &gt; Ca²⁺  &gt; K⁺  &gt; Na⁺, and HCO₃⁻ &gt; Cl⁻ &gt; SO₄²⁻ &gt; NO₃⁻, respectively. The best input combination for WQI model prediction was identified using subset regression analysis. These eight input combinations had high R², ranging from 0.975 to 1, and high Adjusted R² amounts to 0.974–1. The WAWQI range is divided into five categories: excellent (18.18%), good (18.18%), poor (27.27%), very poor (27.27%), and unsuitable (9.09%). The study discovered that increased turbidity concentration, carbonate weathering, and the growth of agricultural and urban-industrial sectors regulate the geographical variance in surface water quality. The correlation results depict that the significant positive correlation has been found between TDS and TH (0.87), Mg²⁺ with turbidity (0.84) and coliform (0.78), Ca²⁺ and coliform (0.72), Cl⁻ and HCO₃⁻ (0.83), and K⁺ and Na⁺ (0.7). Owing to the correlation study, these ions are enriched in the surface water by major anthropogenic activity. While, in the present study, CA and PCA has been used to determine the surface water's governing factors. Differentiation of three clusters based on the sources, hydrogeochemical environment, and reactions between chemical variables by utilizing CA and the results of PCA shows that the first three primary components (PCs) account for 84.76% of the overall variation. Hence, CA and PCA shows the several processes that are the main sources of the ions, such as carbonate, silicate weathering, and evaporate dissolution. Pursuant to the stepwise fitting model, bicarbonate was a non-significant variable for the WQI, whereas turbidity, pH, and coliform were the most significant factors. With a high correlation of 1 and low errors, the results demonstrated that the GPR, stepwise linear regression, and ANN models outperformed the others during the training and testing phases.&#xa0;In contrast, during the training and testing stages, the SVM and FBT models showed the lowest performance. Therefore, the GPR, stepwise regression, and ANN models exhibited low mistakes and a strong correlation during the training and testing phases. In conclusion, the combination of physicochemical characteristics, WQI, CA, PCA, and ML tools to assess the surface water suitability for drinking and irrigation and their regulating variables are beneficial and provides a clear picture of water quality. Future research should improve the data accuracy to increase model precision and extend its applicability to various geographical and environmental settings.</p> Graphical abstract <p></p>