AL has emerged as a critical methodology to optimize the data selection and acquisition for gas adsorption in MOFs and therefore to understand the most important features to predict their adsorption isotherms. Initially in this work, through a systematic application of AL to a dataset of 1800 MOFs, significant strides were made in reducing the dataset while continuously enhancing model performance. This iterative process was marked by a noticeable correlation between the reduction in GP mean uncertainty and the increment in R 2 values through the iterative process, indicating a consistent improvement trend across multiple MOFs. For the first approach with 1800 MOFs, we performed AL on each MOF by setting a convergence criterion of 0.05 mol/kg in the GP mean uncertainty, the resultant amalgamation of training data from diverse MOFs formed a comprehensive dataset encompassing approximately 2.1 million data points, which was used to train a new MLP model. This newly created MLP model showcased precision in predicting the adsorption behaviors of real molecules within the specified alchemical range. It was notable that the model's performance was comparable to the original one. This achievement highlights the efficiency of AL in selecting informative datasets while significantly saving computational resources.