Metal-organic frameworks (MOFs) are crystalline porous materials with exceptional tunability in their structure and properties. MOFs consist of metal nodes connected by organic linkers, resulting in a vast chemical and structural design space. MOFs serve in a broad range of applications, including gas storage, separation, catalysis, sensing, and drug delivery. Among these, methane (CH₄) storage has attracted significant attention, particularly for its potential in cleaner energy technologies. Despite significant advances in the study of MOFs, the discovery of MOFs suitable for gas storage is hindered by the sheer number of possible MOF structures. Both experimental synthesis and computational simulations of adsorption are resource-intensive and cannot feasibly be applied to every candidate material. Consequently, machine learning (ML) models have emerged as powerful tools for accelerating MOF discovery by predicting adsorption. While ML models can greatly speed up the screening process, their predictive accuracy is highly dependent on the quality and diversity of the training data. In this study, key structural properties of MOFs including void fraction (VF), largest cavity diameter (LCD), pore limiting diameter (PLD), and accessible surface area (SA) were used as MOF features to model adsorption of CH4. The VF represents the fraction of a MOF's total volume that is available for gas storage, while the LCD and PLD describe the size of the largest cavities and the narrowest pores within the framework, respectively. SA refers to the accessible surface area where gas molecules can adsorb, which is critical for determining the overall adsorption capacity. All the structural properties values of the CoRE MOFs in this study have been extracted from Bobbitt et al. Selecting a representative subset of MOFs for training a predictive model is non-trivial. A well-chosen dataset should capture the broad structural diversity of the entire MOF database while maintaining relevance to the target property, in this case, methane adsorption. Random sampling, although simple, often fails to cover important regions of the structural-property space comprehensively, leading to biased or underperforming models. Therefore, more advanced methods, such as inducing points (IPs) selection, active learning (AL), and Bayesian optimization (BO), can be useful in identifying the most informative MOFs for model training.