<p>This study investigates the distribution and relationships of heavy metals (Mn, Pb, Zn, Cr, Cd, Cu) in the Styr River through the application of multivariate statistical methods. By combining Pearson correlation analysis, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA), the research offers a detailed assessment of metal dynamics, emphasizing their spatiotemporal variations and associated pollution sources. The dataset highlights seasonal and spatial variability in metal concentrations across sampling sites, reflecting diverse anthropogenic and geochemical influences. Pearson correlation analysis revealed significant positive relationships among metals such as Mn-Zn and Pb–Zn, suggesting shared pathways of mobilization or common sources, including industrial discharge and agricultural runoff. PCA identified three principal components, explaining 79.39% of the dataset variance. Factor 1 grouped Mn, Pb, Zn, and Cr, linking them to industrial and urban pollution, while Factor 2 distinguished Cd and Cu, attributed to localized inputs such as agricultural practices. Factor 3 emphasized sedimentary and redox-driven processes influencing Cr, Zn, and Mn. HCA confirmed spatial clustering of contamination profiles, indicating heterogeneity in pollution sources along the river. These findings underline the utility of multivariate techniques in uncovering complex relationships among environmental pollutants. The study provides a valuable framework for identifying pollution sources and their seasonal impacts, offering actionable insights for improving water quality and fostering sustainable environmental management practices.</p> Graphical Abstract <p></p>