A deep understanding of adsorption processes is essential for the design and optimization of industrial units. Storage of methane adsorbed on activated carbon (AC) at low pressure and room temperature (adsorbed natural gas) has been studied in recent years as an alternative model to compressed natural gas and liquefied natural gas technologies. The current study plays a significant role in modeling CH4 adsorption on different ACs through the optimal multilayer perceptron (MLP) neural network . Therefore, lots of adsorption data points were used for modeling. To optimize the efficiency of a predictive model, two optimization algorithms including LevenbergMarquardt (LM) and Bayesian regularization were utilized to find the optimal models' parameters during prediction analysis. In order to demonstrate the efficiency of the proposed method, it is compared with several other experimental data points. Results of optimizations indicate the superiority of the proposed method over the other techniques, and forecasting error is remarkably reduced. As a result, it was found that the MLP-LM is the more accurate model for estimating CH4 adsorption with root-mean-square error and coefficient of determination of 0.00025 and 0.9921, respectively.