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The Rio de la Plata grassland ecoregion in southern America, which includes Uruguay, south of Brazil and part of Argentina, plays a crucial role in providing feed to livestock in outdoor extensive production systems due to its high plant species richness, chemical composition and annual production. Estimating animal intake in this heterogeneous grassland environment poses a significant challenge. Therefore, our goal was to develop a general linear regression model based on faecal nitrogen excretion (FNe) to estimate organic matter (OM) intake in cattle and sheep fed multi-species native forage using data from different zones of the Rio de la Plata region. We collated data from previous studies conducted based on the same protocol in Brazil and Uruguay, comprising 219 individual observations (72 from cattle and 147 from sheep); 15 data points from sheep in Uruguay remain unpublished. The trials were conducted using metabolism cages and animals were fed fresh or hay native forage. Mixed linear models were developed using R software. The best-fitting model was selected based on the Bayesian information criterion (BIC) and Akaike information criterion (AIC). The predictive accuracy of the fitted OM intake model was evaluated using 5-fold cross-validation. The resulting linear regression model revealed a positive relationship between FNe and OM intake (p < 0.001; R2 = 0.851) across the entire dataset [Intake (g OM/kg BW/day) = 3.335 + 106.321 × FNe (g N/kg BW/day)]. The animal species effect was not significant (p = 0.337). Pearson correlation between predicted and observed values of the animal forage OM intake model was 0.957, with a root mean square error (RMSE) of 1.598, a mean absolute error (MAE) of 1.241 and a concordance correlation coefficient (CCC) of 0.937. In conclusion, our findings highlight that a general linear regression model, developed using combined data from both cattle and sheep, can be used to precisely estimate OM intake using FNe for animals fed multi-species native forage from t he Río de la Plata region.