Monitoring animal productivity is a crucial aspect of farm management and decisionmaking. Sheep breeding is of utmost importance to the agricultural and veterinary industries. However, unlike veterinary epidemiology, applying forecasting models to predict animal productivity remains less explored, and developing reliable predictive modelling is one of the necessary strategies in this regard. This study addresses this gap by focusing on lamb production. To forecast lamb production trends accurately, this study examines the effectiveness of traditional time-series models ARIMA and ETS alongside the neural network autoregressive (NNAR) model, a machine learning-based approach, to identify the most suitable forecasting method. Also, the study aimed to address a critical question about the reliability of predicting productivity with short-time series. The models are trained and evaluated using yearly lamb production data collected from 2003 to 2022. The model exhibiting the lowest corrected Akaike Information Criterion (AICc) was identified as the optimal forecasting model. Its accuracy was confirmed using the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE). Our findings indicate that among the evaluated models, NNAR yielded the most accurate forecasts, reflected in its minimal MAPE and RMSE values compared to ARIMA and ETS. This highlights its superior predictive performance for short time series in annual sheep production. While ARIMA outperformed ETS on most evaluation metrics, NNAR consistently delivered the best results. Notably, NNAR maintained high accuracy despite the small sample size (20 observations), whereas ARIMA may require a larger dataset to perform optimally. Overall, these models are suitable for time series analysis and hold promises for broader application in animal science to enhance forecasting and decisionmaking.