The present investigation aimed to compare the performance of two data mining algorithms, Automatic Linear Modeling (ALM) and Artificial Neural Network (ANN), and the traditional Multivariate Linear Regression model (MLR) to predict the egg weight (EWT) of Mallard duck from some egg traits including egg length (EL), egg width (EWd), egg shape index (ESI), eggshell weight (ESW), albumen weight (AW), albumen height (AH), yolk weight (YW), yolk height (YH), yolk diameter (YD), and Haugh unit (HU). The Pearson correlation between observed and predicted values (r), coefficient of determination (R²), adjusted coefficient of determination (R²_adj), Root Mean Squared Error (RMSE), and Relative Approximation Error (RAE) were used to estimate model performance. EWT had a strong correlation with egg dimensions (EL and EWd, r= 0,752 and 0,790, respectively), AW (r= 0,815), and YW (r= 0,784). The R²_adj values were 0.972, 0.963, and 0.960 for ALR, ANN, and MLR models, respectively. RMSE values were 0,924, 1.067, and 1.090 for ALR, ANN, and MLR models, respectively. Overall, all three models provided nearly similar results. However, the ALM algorithm showed better predictive performance in comparison to MLR and ANN models and could be considered the most appropriate for the prediction of egg weight in Mallard duck.