Regional hydrochemical variations and their evolution mechanisms have been a common concern for a long time, especially in fragile ecological areas such as the Qaidam Basin. However, previous studies have conducted hydrochemical investigations mostly based on aquifer properties or geomorphic conditions to reveal hydrochemical characteristics and evolution mechanisms. In this study, we systemically collected and investigated hydrochemical characteristics by integrating machine learning algorithms and multivariate statistical analyses to unravel regional variability and evolutionary processes along the groundwater flow in the Qaidam Basin. The results deciphered more detailed and data-driven regional hydrochemical patterns. The hydrochemical type evolved from HCO3·Cl-Ca·Mg·Na type in the recharge area to Cl-Mg·Na type in the distal discharge area and displayed a two-step variation, characterized by the accumulation of ionic components. A total of 8 clusters were identified from the SOM-KM output, representing distinct hydrochemical characteristics. Surface water and underground water showed similar hydrochemical evolutionary paths. The predominant hydrochemical process shifted from water–rock interaction to evaporation and concentration along the groundwater flow path. Cation exchange reactions were responsible for abnormal Mg2+/Na+ ratio and asynchronous Na+ and Cl− variations. We obtained a deeper and more comprehensive understanding of the regional hydrochemical variations and evolutionary processes within the Golmud River Watershed. This study showed that combining novel data mining techniques and traditional hydrochemical approaches has great potential and promising prospects regarding the hydrochemical variation and evolution mechanism based on hydrochemical traits. The outcomes of this study are significant for hydrochemical evolution mechanisms and groundwater management in arid areas worldwide.