<p>A comprehensive understanding of river water quality variations is crucial for sustainable water resource management. This study demonstrates the application of multivariate statistical techniques to identify the significant water quality variables by examining 17 water quality parameters across 11 monitoring sites along the Sabarmati River. Analysis of variance (ANOVA), cluster analysis (CA), principal component analysis (PCA)/factor analysis (FA), and box-whisker plots were employed to interpret the dataset. ANOVA results reveal significant variations in water quality parameters among the monitoring stations. The box-whisker plots illustrate the spatial–temporal variability in the water quality. CA categorizes the sites as less polluted (LP), moderately polluted (MP), and highly polluted (HP) based on their similarities. PCA applied to the spatially distinguished sites produces four PCs for LP and HP sites and three PCs for MP sites, explaining 62.38% (LP), 60.32%, (MP), and 67.18% (HP) of the total variance. FA highlights the significance of the water quality parameters across the spatially distinguished sites. TDS, Cl, COD, DO, sulphate, and nitrate were significant for all sites, indicating the influence of runoff and anthropogenic activities. Sample temperature predominated at LP and MP sites, while pH and NH₃-N were more prevalent at MP and HP sites, underscoring the sources of contamination at the respective locations. The higher levels of chemical pollution at HP sites indicate wastewater discharges. The study’s findings can help predict future water quality and facilitate effective river resource management.</p> Graphical Abstract <p></p>