One of the important parameters in economic study of energy sources and bioenergy is higher heating value (HHV). In this investigation, adaptive neuro fuzzy inference system (ANFIS) was applied as a novel method to predict HHV of biomass in terms of fixed carbon (FC), ash content (ASH), and volatile matters (VMs). Due to the fact that experimental investigations are time- and cost-consuming, this investigation was selected purely computational and a total number of 350 experimental data were extracted from literature for different steps of modeling. The proposed algorithm was evaluated by statistical indexes such as coefficient of determination (R-2), root mean squared error (RMSE), and average absolute relative deviation (AARD), which are 0.90757, 1.1792, and 5.266, respectively. The reported indexes showed that ANFIS-particle swarm optimization can be used as a novel computational approach for prediction of HHV as function of proximate analysis.