Abstract The present analysis was conducted as the first study to investigate the biochemical methane potential of four different agro-industrial wastewaters originating from chocolate, slaughterhouse, gum, and beet sugar industries under the same anaerobic fermentation conditions. To the best of our knowledge, no previous study has specifically attempted to pinpoint a hybrid programming strategy for making a quantitative description of the anaerobic biodegradability of these waste streams. Thus, considering the scarcity of the literature in this field, a comprehensive study was conducted to evaluate the amount of bio-methane obtainable from the investigated organic wastes and to predict their kinetics using three different sigmoidal microbial growth curve models (modified Gompertz equation, transference function (reaction curve-type model), and logistic function) within the framework an original MATLAB®-based coding scheme. The results showed that methane productions started immediately after 4 h of incubation for all substrates and reached their maximum rates of 118, 116, 108, 34 mL CH4/g VS/day, respectively, for wastewaters from chocolate, slaughterhouse, gum, and beet sugar industries. The corrected mean steady state methane contents were 61.7%, 73.4%, 62.8%, and 62.1% in the respective order. The highest methane yield (943 mL CH4/g VS) was obtained from the slaughterhouse wastewater, and this value was 1.32, 1.58, and 4.56 times higher than those obtained in the anaerobic digestion of chocolate, gum, and beet sugar wastewaters, respectively. Among the three kinetic models tested, the logistic function best explained the behavior of the observed data of all substrates using a Quasi-Newton cubic line search procedure (R2 = 0.987–0.996) with minimum number of non-linear iterations and function counts. Deviations between the measured and the outputs of the best-fit kinetic model were less than 4.3% in prediction of methane production potentials, suggesting that the proposed computational methodology could be used as a well-suited and robust approach for modeling and optimization of a highly non-linear biosystem.