Manure nitrogen (N) from cattle production facilities can lead to negative environmental effects, such as contribution to greenhouse gas emissions, leaching and runoff to aqueous ecosystems leading to eutrophication, and acid rain. To mitigate these effects and to improve the efficiency of N use, accurate prediction of N excretion and secretions are required. A genetic algorithm was implemented to select models to predict fecal, urinary, and total manure N excretions, and milk N secretions from 3 classes of animals: lactating dairy cows, heifers and dry cows, and steers. Two tiers of model classes were developed for each category of animals based on model input requirements. A total of 6 models for heifers and dry cows and steers and an additional 2 models for lactating dairy cattle were developed. Evaluation of the models using K-fold cross validation based on all data and using the most recent 6 yr of data showed better prediction for total manure N and fecal N compared with urinary N excretion, which was the most variable response in the database. Compared with extant models from the literature, the models developed in this study resulted in a significant improvement in prediction error for fecal and urinary N excretions from lactating cows. For total manure production by lactating cows, extant and new models were comparable in their prediction ability. Both proposed and extant models performed better than the prediction methods used by the US Environmental Protection Agency for the national inventory of greenhouse gases. Therefore, the proposed models are recommended for use in estimation of manure N from various classes of animals.