Background: Early identification of metabolic dysregulation remains a public health priority, yet current diagnostic criteria for Pre-Metabolic Syndrome (PreMetSyn) require multiple laboratory assessments that limit feasibility in low-resource settings. Serum uric acid (UA) has been proposed as a low-cost screening alternative, but its diagnostic performance for detecting preclinical metabolic dysfunction is not well understood. Methods: This secondary analysis used data from 3,255 adults aged 18–79 years from NHANES 2011–2018. PreMetSyn was defined as the presence of exactly two NCEP ATP III risk factors. UA was evaluated as (1) a continuous measure, (2) a categorical hyperuricemia (HYPUR) variable, and (3) a theoretical pre-hyperuricemia (PREHYP) classification derived from published PreMetSyn means. Survey-adjusted sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and agreement statistics (observed agreement, Cohen’s κ, PABAK, prevalence index, and bias index) were computed overall and within sociodemographic subgroups. Fully adjusted survey-weighted logistic regression models were used to estimate associations between UA and PreMetSyn. Results: Diagnostic performance was modest for both UA classifications. For HYPUR, sensitivity was 18.8% and specificity was 90.9%; for PREHYP, sensitivity was 25.4% and specificity was 86.3%. PPV ranged from 48.8% to 51.5%, and NPV ranged from 68.5% to 69.2%. Agreement was low across all analyses (κ = 0.115–0.133), although overall observed agreement approached 0.65–0.70 across subgroups. In logistic regression models, continuous UA was strongly associated with PreMetSyn (AOR = 1.53; 95% CI: 1.38–1.70). Categorized exposures also showed significant associations: PREHYP (AOR = 2.36; 95% CI: 1.77–3.15) and HYPUR (AOR = 2.61; 95% CI: 1.97–3.47). Conclusions: Although UA demonstrated strong statistical associations with PreMetSyn, its diagnostic performance as a standalone screening tool was limited by low sensitivity and modest agreement under current PreMetSyn definitions. The robust linear association between UA and metabolic risk highlights its potential value within expanded or composite screening frameworks. Future research should employ receiver operating characteristic–based methods to derive population-specific UA cut points and clarify UA’s role in early metabolic risk identification.