The long-term spatiotemporal assessment of groundwater resources through robust clustering techniques can be used to promote remediation measures for groundwater depletion and contamination. To fully understand the variability of groundwater quantity and quality due to anthropogenic activities and climate changes, a new ensemble clustering framework based on the Combining Multiple Clusters via Similarity Graph (COMUSA) method was developed. This new approach was applied and evaluated in the context of groundwater well systems on the Ghorveh-Dehgolan Plain (GDP), which is located in western Iran, for groundwater level (GWL) and 13 physicochemical parameters during four periods (the average of data from 1988–1990, 1997–1999, 2006–2008, and 2015–2017). The classification was confirmed by using the cluster validity index of the silhouette coefficient (SC), which indicated that the cluster ensemble method could improve the performance of individual clustering methods for groundwater quantity and quality by up to 12% and 20%, respectively. Piper plots, US Salinity Laboratory Staff (USSL) diagrams, and the pollution index of groundwater (PIG) were assessed for all clusters of physicochemical variables to analyse groundwater suitability for drinking and irrigation purposes. The results of the cluster ensemble showed that a critical pattern of groundwater depletion occurred in the western half of the GDP, while the eastern part was recognized as the most polluted zone on the plain. It could be concluded that the decline in GWL was not the only reason for the increase in groundwater quality variables, but other factors, such as noticeable cropland expansion and the overuse of chemical fertilizers and pesticides, were also influential factors related to these patterns. Taken together, the results of this study contribute to better recognizing the spatiotemporal changes in groundwater quantity and quality under the intense pressure of anthropogenic activities.