The elemental composition of coal and biomass provides significant parameters used in the design of almost all energy conversion systems and projects. The laboratory tests to determine the elemental composition of coal and biomass is time-consuming and costly. However, limited research has suggested that there is a correlation between parameters obtained from elemental and proximate analyses of these materials. In this study, some predictive models of the elemental composition of coal and biomass using soft computing and regression analyses have been developed. Thirty-one samples including parameters of elemental and proximate analyses were used during the analyses to develop multiple prediction models. Dependent variables for multiple prediction models were selected as carbon, hydrogen, and oxygen. Using volatile matter, fixed carbon, moisture and ash contents as independent variables, three different prediction models were developed for each dependent parameter using ANFIS, ANN, and MLR. In addition, a routine for selecting the best predictive model was suggested in the study. The reliability of the established models was tested by using various prediction performance indices and the models were found to be satisfactory. Therefore, the developed models can be used to determine the elemental composition of coal and biomass for practical purposes.