In this work, a combination of isotopic and hydrogeochemical data of a karstic region was clustered with four distinct clustering analysis (CA) methods to study water evolution in a vulnerable karstic region to improve protection, sustainability, and enhanced water resource management. Four CA methods, including hierarchical cluster analysis (HCA), K-means (KM), and fuzzy logic CA methods, fuzzy C-mean (FCM), and genetic K-means (GKM), have been utilized to analyze hydrochemical, chemical, and isotopic datasets, including dissolved inorganic carbon (DIC), δ13C-DIC, δ18O, and δ2H datasets of water resources of Paveh-Javanrud (PV-JR) karstic region, located at the western border of Iran and Iraq countries. The utilized dataset contains 34 water samples with varied origination to evaluate the performance of each model and find the best method based on a meaningful categorization of geological, hydrogeochemical, and isotopic characteristics. Finally, the best model results were matched graphically with developed geospatial graphs to visualize the correlation between the region’s water resources. Accordingly, the FCM and GKM methods represent the same, yet meaningful results and have the best performance among the four methods. It was also identified that the PV-JR water resources could be generally categorized into five distinct clusters, including FC1 to FC5 and GK1 to GK5, of which two clusters that have mixing, two clusters with solo-origination and no sign of mixing, and finally, a seasonal spring which is categorized as a separate cluster. Potentially, studying water resources via theoretical methods combined with considering isotope hydrology is of particular interest since solving the environmental issues related to karstic regions and their water resource management are shared concerns in most arid and semi-arid countries, especially in the Middle East as this study, thus could lay a basis for the following scientific attempts involving hydrogeochemical studies and advanced statistical analysis.

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