Co-gasification is a process that converts coal and biomass into useful products, such as syngas. Analytical and numerical approaches for modeling co-gasification process either require enormous amount of time or make a lot of assumptions which reduce consistency of the models in practical applications. Artificial Intelligence based modeling methods are used to simulate and to make predictions of outcomes of the co-gasification process. Even though previous studies result in successful modelling for specific cases, limited selection of methods and lack of implementation of cross-validation techniques causes insufficiency to explain unbiased performance evaluations and up-scale usability of the methods. In this paper, six different regression methods are employed to predict outputs of co-gasification process using a dataset containing 56 observations. Moreover, the original dataset is randomly resampled so that each model’s generalization ability is further assessed. The prediction performance of the proposed techniques on both datasets is evaluated and practical usability is discussed.