In this study of gas adsorption in MOFs, GPR was utilized to model the relationship between various features associated with MOFs and the adsorption of specific molecules. On the navigation of fugacity and alchemical molecules across 1800 MOFs, the applied GP model trained individual GPR models for each MOF to predict the adsorption behavior of different molecules within that MOF. In the scenario involving the navigation of fugacity, alchemical molecules, and the 3445 MOFs represented by the principal component derived from their textural properties, a single GP model is trained to predict adsorption. The complete training process was carried out through AL iterations strategically selecting data points that improve the predictive accuracy of the model, ultimately reducing computational cost to generate the training data. The GPflow library was used for implementing the AL workflow and the GP used the rational quadratic (RQ) kernel .