Background/Objectives: Metabolic syndrome (MetS) is a common cardiometabolic condition characterized by clustering of metabolic risk factors. Early identification in routine clinical practice remains challenging. This study aimed to explore the performance of artificial intelligence (AI)–based models for the prediction of MetS using routinely available clinical and laboratory parameters, with a particular focus on serum uric acid. Methods: This retrospective study was conducted using a publicly available dataset including 640 individuals (320 MetS-positive and 320 MetS-negative). MetS was defined according to modified National Cholesterol Education Program Adult Treatment Panel III criteria. Machine learning models were developed using combinations of age, sex, body mass index, uric acid, and albumin. Logistic regression, support vector machines, decision trees, k-nearest neighbors, and random forest algorithms were evaluated. Model performance was assessed using accuracy, area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score. Internal validation was performed using 5-fold cross-validation. Results: Individuals with MetS had significantly higher uric acid levels compared to those without MetS (6.02 ± 1.57 vs. 2.84 ± 0.35 mg/dL, p < 0.001), while albumin levels were similar. The evaluated models demonstrated high classification performance within the study dataset, with accuracy values up to 0.99 and AUC values up to 0.99. Models using a limited number of variables, particularly body mass index, age, and uric acid, showed consistent performance. Models based on uric acid and albumin achieved accuracy values around 0.98, while single-variable models using uric acid alone achieved accuracy of 0.97. Conclusions: AI-based models using a limited set of routinely available clinical and laboratory parameters demonstrated the potential to classify MetS. Serum uric acid consistently emerged as one of the most discriminative variables, supporting its potential role as a clinically informative and accessible biomarker in MetS classification.