This study investigates the utility of the Triglyceride-glucose(TyG) index and Cardiometabolic Index(CMI) as predictors for diagnosing metabolic syndrome. The study involved 1970 males, 1459 females, totaling 3429 participants who underwent health checkups at P Hospital in Busan between January 2023 and June 2023. Metabolic syndrome diagnosis was based on the presence of 3 or more risk factors out of the 5 criteria outlined by the American Heart Association/National Heart, Lung, and Blood Institute(AHA/NHLBI), and participants with 2 or fewer risk factors were categorized as normal. Statistical analyses included independent sample t-tests, chi-square tests, Pearson's correlation analysis, Receiver Operating Characteristic(ROC) curve analysis, and logistic regression analysis, using the Statistical Package for the Social Sciences(SPSS) program. Significance was established at p<0.05. The comparison revealed that the metabolic syndrome group exhibited attributes such as advanced age, male gender, elevated systolic and diastolic blood pressures, high blood sugar, elevated triglycerides, reduced LDL-C, elevated HDL-C, higher Cardiometabolic Index, Triglyceride-glucose index, and components linked to abdominal obesity. Pearson correlation analysis showed strong positive correlations between waist circumference/height ratio, waist circumference, Cardiometabolic Index, and triglycerides. Weak positive correlations were observed between LDL-C, body mass index, and Cardiometabolic index, while a strong negative correlation was found between Cardiometabolic Index and HDL-C. ROC analysis indicated that the Cardiometabolic Index(CMI), Triglyceride-glucose(TyG) index, and waist circumference demonstrated the highest Area Under the Curve(AUC) values, indicating their efficacy in diagnosing metabolic syndrome. Optimal cut-off values were determined as >1.34, >8.86, and >84.5 for the Cardiometabolic Index, Triglyceride-glucose index, and waist circumference, respectively. Logistic regression analysis revealed significant differences for age(p=0.037), waist circumference(p<0.001), systolic blood pressure(p<0.001), triglycerides(p<0.001), LDL-C(p=0.028), fasting blood sugar(p<0.001), Cardiometabolic Index(p<0.001), and Triglyceride-glucose index (p<0.001). The odds ratios for these variables were 1.015, 1.179, 1.090, 3.03, and 69.16, respectively. In conclusion, the Cardiometabolic Index and Triglyceride-glucose index are robust predictive indicators closely associated with metabolic syndrome diagnosis, and waist circumference is identified as an excellent predictor. Integrating these variables into clinical practice holds the potential for enhancing early diagnosis and prevention of metabolic syndrome.