Accurate information on metabolizable energy and true digestible amino acid (TDAA) content of sorghum grain is important in order to formulate sorghum-based poultry diets accurately. Estimates of ingredient nutritional values using bioassay methods require live birds and special facilities, which are time-consuming and costly. Accordingly, prediction by mathematical models would be of some considerable benefit. Sixty-eight samples of sorghum grain, representing 32 different varieties, were used to test the correlation between TDAA and nitrogen-corrected true metabolizable energy (TMEn) with total essential amino acids. Two methods of multiple linear regressions (MLR) and artificial neural network (ANN) models were used to find the relationship between total amino acids (model inputs) with TDAA and TMEn contents (model outputs) in sorghum grain. The fitness of the models was tested using R2, mean square (MS) error and bias. There is a strong relationship between total amino acid concentration with both TDAA and TMEn content in sorghum grain. The TDAA and TMEn values were more accurately estimated by ANN model compared to values obtained from the MLR model. The R2 values corresponding to testing and training of the ANN model showed a higher accuracy of prediction than the equation constructed by MLR method. Based on the experimental evidence, it is concluded that the TDAA and TMEn values in sorghum grain can be predicted from total essential amino acids using ANN models. Consequently, this method provides an opportunity to reduce the risk of formulating an unbalanced TDAA diet for poultry.