The concentration of sulfate in global groundwater has been observed a significant upward trend in recent years. Excessive sulfate levels contribute to increased groundwater salinity and acidification, thereby posing a threat to the human health and ecological balance. For effective groundwater pollution management and control, accurately quantifying the sources of sulfate pollution remains a challenge. This research integrates the Self-Organizing Maps (SOM) clustering method to enhance the accuracy of the Bayesian isotope mixing model (MixSIAR) in quantifying the contribution rate of groundwater sulfate. During the dry season, sulfate (SO