One alternative to improve feed quality is to combine the main feed with forages which are more economical in cost but contain high protein sources, such as sorghum. Production estimation is essential because it will determine the sustainability of the feed. This study aimed to estimate the amount of sorghum production using support vector regression (SVR). Several stages of this research are collecting data, preprocessing, modelling, and evaluation. The dataset used and the input for this SVR algorithm model is field observation data. The kernels used in the SVR algorithm modelling are linear, Polynomial, and RBF. Sorghum production estimation using SVR has a performance evaluation value that refers to the root mean square error (RMSE). The result of this research is that the model obtained from the SVR algorithm can estimate sorghum production with performance evaluation values using R2, mean absolute error (MAE), mean absolute percentage error (MAPE), and RMSE. The best results on the Polynomial kernel are R2=0.7841, MAE=0.0681, MAPE=0.46641, and RMSE=0.1006. This study shows that the classification model obtained from the SVR algorithm with Kernel Polynomial is the best model for estimating sorghum production.