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Purpose: This study analyzed four-year data (2021~2024) on cardiovascular disease risk among workers in a manufacturing factory, using generalized estimating equations (GEE) and linear mixed-effects models to examine temporal changes and identify factors influencing cardiovascular disease (CVD) risk. Methods: The study included 546 participants who underwent health examinations in March and September each year from 2021 to 2024. CVD risk was assessed using physical examination results. Data were analyzed with R 4.5.1 (R Foundation for Statistical Computing, Vienna, Austria) and SPSS/WIN 25.0. Results: Time-effect analysis of CVD risk components showed that systolic blood pressure significantly increased over time (β=.84, p<.001), while diastolic blood pressure significantly decreased (β=-.46, p=.004). Body mass index (BMI) also significantly increased (β=.11, p=.002). In the GEE logistic regression analysis, Male workers had 2.38 times higher odds of being in the high-risk group compared to females (OR=2.38, p=.009). Higher systolic blood pressure (OR=2.52, p<.001) and fasting blood glucose (OR=8.66, p<.001) were significant predictors of high CVD risk. Conclusion: When developing and implementing workplace interventions to reduce CVD risk among manufacturing workers, key predictors such as sex, blood pressure, and blood glucose should be considered for effective risk stratification and targeted prevention. This study is important because it examined both temporal changes and influencing factors of CVD risk using four-year longitudinal data.