Public-Private Partnership (PPP) projects have become an effective mechanism for delivering large-scale infrastructure and development initiatives by integrating public-sector objectives with private-sector resources, expertise, and risk-sharing capabilities. In particular, R&D-oriented PPP projects play a significant role in supporting urban development through innovation-driven investments, improved service quality, and more efficient allocation of financial, human, and technical resources. However, selecting the most appropriate PPP project is a complex multi-criteria decision-making (MCDM) problem, especially when evaluation criteria are interdependent and decision information is subject to uncertainty. To address this issue, this study proposes an intuitionistic fuzzy hybrid weighting and ranking approach for R&D-oriented PPP project selection with interdependent criteria. This study proposes an integrated decision-support framework for the selection of R&D-oriented PPP projects. The framework systematically combines well-established intuitionistic fuzzy methods—IF-ANP and IF-DEMATEL for criteria weighting, IF-CS for decision-maker weighting, and an intuitionistic fuzzy ranking procedure—to address uncertainty and interdependencies among criteria in a realistic case study. To validate the applicability of the proposed approach, a case study in urban development is conducted. The results show that the proposed model provides a practical and robust decision-support tool for project managers by improving the handling of uncertainty, interdependence, and group decision-making. In addition, a comparative analysis with an existing method from the literature confirms the efficiency and robustness of the proposed framework.