This study developed two Artificial Neural Network (ANN) tools for predicting sugar yields from inorganic salt-based pretreatment of lignocellulosic biomass. Pretreatment data from 90 experimental runs with 8 different input conditions were used to develop a microwave-based and a steam-based model. Both models exhibited high coefficients of determination (R-2) of 0.97. Knowledge extraction revealed reducing sugar yields from the steam- and microwave-based models exhibited high sensitivity to both salt and alkali concentration. These models may be employed as initial screening tools in lignocellulosic bioprocesses, thereby potentially enhancing the economic and productivity of lignocellulosic-based bioprocesses.