The “Thermal-dissolution based carbon enrichment” was proven as an efficient and homogenizing treatment method in converting biomass wastes into similar high-quality carbon materials. However, their yields varied significantly with respect to the different experimental parameters employed. It is therefore imperative to establish the correlation between product yield and experimental parameters for material selection and condition optimization. In this study, Adaboost was coupled with an artificial neural network algorithm to precisely describe the abovementioned correlation. The results demonstrated the effectiveness of this model through its outstanding predicting performance for all the products, especially, the coefficient of determination in predicting the yield of Residue was as high as 0.97. Additionally, the coupling effect of temperature and time was observed. This study not only validates a close correlation between selected experimental parameters and product yields, but also provides a quick and reliable way for material selection and condition optimization.