Among the responsibilities of senior managers in each project-based organization, project portfolio selection (PPS) is critical. Practical optimization and decision-making models play a vital role in facilitating managerial decision-making. In this respect, the current study develops a new data-driven multi-criteria decision-making (MCDM) model for group decision-making in the PPS within the framework of project portfolio management. Mining-specific criteria will help enhance project evaluation and selection, allowing the project manager to make more accurate and efficient decisions. By incorporating interval-valued fuzzy sets (IVFSs) into the decision-making process, this paper addresses uncertainties and ambiguities in experts' judgments, making evaluations more realistic and flexible. A new interval-valued fuzzy weighting method, IVF-LMAW, is developed to determine the relative importance of the criteria, and a new IVF method, IVF-AURA, is presented to prioritize projects, offering higher accuracy and stability than conventional decision approaches. To validate the accuracy and robustness of the model, the criteria weights have been subjected to a sensitivity analysis, and the resulting rankings have been examined. To highlight the practical applicability of the model, a real case study in the mining industry has been performed on a set of projects. The results show that the proposed approach can efficiently, transparently, and reliably support the PPS process by simultaneously considering both financial and non-financial dimensions.