The objective of this research was to identify geochemical anomalies associated with mineralization in the Feizabad region using deep embedded clustering (DEC). Two approaches were employed: using all geochemical elements (non-selective) and using factor scores (selective) to construct input data. Results showed that the non-selective approach was highly successful in identifying anomalies, not only confirming known mineral occurrences (KMO) but also introducing new exploration targets. Additionally, model evaluation using the success rate curve (SRC) confirmed its high predictive power. This method is particularly useful in green field. The DEC model, by combining deep neural networks (DNN) and clustering algorithms, reveals complex patterns in geochemical data. This algorithm increases clustering accuracy through automatic data clustering, gradual input of large data, and compatibility with imbalanced data. Furthermore, by simultaneously integrating feature learning and clustering, it provides higher optimization and accuracy. The anomalies identified by the DEC model are consistent with granodiorite units in the region and may indicate the potential for mineralization in the study area. Additionally, the identified targets are associated with granitoids, hydrothermal alteration zones, and intrusive rocks.