This study aims to evaluate the relationships between internal and external egg quality traits and the albumen index in eggs obtained from Star-53 French Pekin ducks. The albumen index was selected as the dependent variable, while egg weight, width, length, shape index, and Haugh unit were included as independent variables in the model. Initially, a multiple linear regression model was constructed using the Least Squares Methods (LSM), resulting in a high coefficient of determination (R² = 0.96). However, the presence of high correlations among independent variables indicated a multicollinearity, as evidenced by high Variance Inflation Factor (VIF ≥ 10) and low Tolerance values. To address the issue of multicollinearity, Ridge, LASSO, and Liu regression methods were applied. In the models estimated using these regularized regression techniques, the coefficient of determination decreased to approximately 88 %, suggesting improved generalizability and reduced overfitting. Comparative analyses revealed that the Ridge regression model had the lowest values in terms of information criteria (Akaike Information Criterion - AIC, corrected Akaike Information Criterion - CAIC, Bayesian Information Criterion - BIC), making it the most consistent and reliable modeling strategy under multicollinearity problem conditions. The findings indicate that external quality traits significantly affect the albumen index and support the use of external parameters as potential indicators of internal egg quality. In conclusion, the use of parametric regularization methods in biometric datasets characterized by high multicollinearity problem offers more reliable and predictive models compared to classical approaches. Future studies are encouraged to integrate machine learning-based methods into similar data structures to enhance predictive performance further.