A dynamic modeling framework based on an intelligent approach is proposed to identify the complex behaviors of solid-gas sorption systems. An experimental system was built and tested to assist in developing a model of the system performance during the adsorption and desorption processes. The variations in the thermal effects and gaseous environment accompanying the reactions were considered when designing the model. An optimization platform based on a multi-population genetic algorithm and artificial criteria was established to identify the modeling coefficients and quantify the effects of condition changes on the reactions. The calibration of the simulation results against the tested data showed good accuracy, where the coefficient of determination was greater than 0.988. The outcome of this study could provide a modeling basis for the optimization of solid-gas sorption systems and contribute a potential tool for uncovering key characteristics associated with materials and components.