The potential of a major bioresource (Peanut hull) for biogas generation was evaluated. A sample was pretreated using combinations of mechanical and thermo-alkaline procedures using the Central Composite Design (CCD) for the optimization of the pretreatment temperature and time while another sample was treated without thermo-alkaline methods. The physico-chemical and microbial characteristics of the A. hypogaea hull and the rumen contents were carried out using standard methods. The actual biogas yields were 1739.20 m(3)/kg TSfed and 1100.50 m(3)/kg TSfed with desirability values of 91 and 100% for the pretreated and untreated experiments respectively. The methane and carbon dioxide content of biogas from both experiments as revealed by Gas chromatography were 61.5 +/- 2.5%; 24 +/- 1% and 51 +/- 2%; 25 2% respectively. The optimization of important process parameters in the anaerobic digestion were done using CCD of Response Surface Methodology (RSM) and the Artificial Neural Networks (ANNs) and the optimal values for each of the five major parameters optimized are as follows: Temperature = 30.00 degrees C, pH = 7.50, Retention time = 30.00 day, Total solids =12.00 g/kg and Volatile solids = 4.00 g/kg. Taking these values into account, the predicted biogas yield for RSM was 1819.89 m(3)/kg TSfed and 1743.6 m(3)/kg TSfed for ANNs in the thermo-alkaline pretreated experiment. For the experiment without pretreatment, the RSM predicted yield was 1119.54 m(3)/kg TSfed while that of ANNs was 1103.40 m(3)/kg TSfed. In all there was a 38.5% increase in predicted biogas yield in the experiment with pretreatment over that of untreated A. hypogaea hull. Based on the coefficient of determination (R-2), the mean error and predicted biogas yields, ANNs was found more accurate and is recommended for the optimization of biogas generation from the substrate used in this study. Further usage of peanut hull for biofuels is encouraged. (C) 2017 Elsevier Ltd. All rights reserved.