For sustainable animal breeding, body weight and morphological measurements are taken. In this study, a multi-layer feed-forward neural network model was created utilizing several morphological measures to estimate body weight in Southern Anatolian Red Cattle. The withers height, body length, chest girth, and rump width were defined as inputs while body weight was defined as a single output in the feed-forward neural network architecture. Network training was performed using Levenberg-Marquardt, Scaled Conjugate Gradient, and Bayesian Regularization algorithms. The linear function at the output and the hyperbolic tangent sigmoid function at the input of the hidden layer were both maintained constant, and the number of neurons in the hidden layer was varied to search for the optimal geometry for each transfer function. Feed-forward neural network optimization was performed using MSE and R² performance criteria. The performance metrics RMSE, MAE, MAPE%, and VAF% were used to compare the optimized feed-forward neural network models and predict the best model. The neural network models model created with the Bayesian Regularization algorithm was confirmed to be the best model. All morphological measurements as predictors had a high correlation (r < 0.8) with body weight estimation, with the greatest correlation among the morphological measurements being 0.947 between chest girth and withers height (p < 0.001). As a result, the optimum feed-forward neural network model was determined to be the Bayesian Regularization back-propagation algorithm. The proposed feed-forward neural network model has been proven to accurately predict body weight in Southern Anatolian Red Cattle (SAR) using input and output variables within the study's data range.