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Metabolic syndrome (MetS) is a worldwide public health challenge. Accumulating evidence implicates elevated serum ferritin and disruptions in iron metabolism as potential elements linked to an increased risk of MetS. This study investigates the relationship between iron homeostasis-including hepcidin levels, serum iron concentration, unsaturated iron-binding capacity (UIBC), and the hepcidin/ferritin (H/F) ratio-and MetS. In this descriptive cross-sectional study, 209 participants aged 24-70 were categorized into two groups: 103 with MetS and 106 without MetS. All participants underwent medical assessment, including anthropometric measures, indices of glycemic control, lipid profiles, and iron-related parameters. Participants were further stratified by the Homeostasis Model Assessment-Insulin Resistance index into three subgroups: insulin-sensitive (IS) (<1.9), early insulin resistance (EIR) (>1.9 to <2.9), and significant insulin resistance (SIR) (>2.9). Notable increments in serum ferritin and hepcidin were observed in the SIR group relative to the IS and EIR groups, with a significant association between metabolic parameters. The UIBC and serum ferritin emerged as significant predictors of MetS, particularly in men, with an area under the curve (AUC) of 0.753 and 0.792, respectively ( p ≤ 0.001). In contrast, hepcidin was notably correlated with MetS in women, with an AUC of 0.655 ( p = 0.007). The H/F ratio showed superior predictive capability for MetS across both sexes (at cutoff level = 0.67). Among women, this ratio had an AUC of 0.639 ( p = 0.015), and for men, it had an AUC of 0.792 ( p < 0.001). Hypertension proved an independent risk factor for MetS, affirming its role in metabolic dysregulation. The findings highlight a significant interconnection between iron homeostasis parameters and MetS, with sex-specific variations underscoring the importance of personalized diagnostic criteria. The crucial role of the H/F ratio and the UIBC as emerging predictive markers for MetS indicates their potential utility in identifying at-risk individuals. Further longitudinal research is essential to establish causality and explore the interplay between these biomarkers and MetS.