<p>In this study, 163 stream sediment samples were collected from the sub-catchment of Khomain Dehno, and 39 samples were taken from the average elemental concentration at the outlet of the catchment. A total of thirteen elements were analyzed using multi-metal factor analysis, multi-metal fractal analysis, and composite halo methods. Through stepwise factor analysis, three distinct element groups were identified: Mn-Mo-Zr, Zn-Pb-As-Cd and Fe-Cu-Ti. The elements in the first and third groups were associated with mineralization and accessory minerals found in granite intrusive masses of the region, while the elements in the second group corresponded to Pb and Zn mineralization in Cretaceous limestones. Minerals related to the first and third groups are observed alongside pyrolusite, iron oxides, malachite, and chalcopyrite, whereas minerals associated with the second group include smithsonite, cerussite, and galena. Among these groups, Pb-Zn-Cu mineralization potential was identified as the most significant. To identify the best geochemical anomaly separation method, three techniques were applied: Effective surface method, Metal effective surface method and Coefficient areal association method. The results indicated that the coefficient areal association method was the most effective for anomaly separation, especially for: The Zn + Pb + As + Cd halo (fuzzy value = 0.73), The Zn × Pb × As × Cd halo (fuzzy value = 0.71), the Zn-Pb-As-Cd factor analysis (fuzzy value = 0.73) and the Zn × Pb × As × Cd concentration-area fractal (fuzzy value = 0.75). These results suggest that the coefficient areal association method outperforms the effective surface and metal effective surface methods in identifying geochemical anomalies. Additionally, the Zn × Pb × As × Cd concentration-area fractal model and the Zn-Pb-As-Cd factor analysis, with the highest fuzzy coefficient areal association values of 0.78 and 0.76, respectively, were identified as the best methods for distinguishing anomalies from the background.</p>