Digital knowledge sharing and university–industry collaboration in emerging technologies using SEM–machine learning approach

University–Industry Collaboration (UIC) is central to innovation systems, yet its effectiveness remains constrained in many developing economies due to weak knowledge integration mechanisms and fragmented institutional support. This study examines how emerging digital technologies; Artificial intelligence integration (AII), blockchain transparency (BCT), digital infrastructure quality (DIQ), and organizational readiness (OR), influence UIC performance through the mediating role of digital knowledge sharing (DKS) and the moderating effect of government policy support (GPS). Drawing on the technology organization environment (TOE) framework, the Knowledge based view (KBV), and dynamic capability theory (DCT), a research model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) on survey data collected from 406 academic and industry stakeholders involved in UIC activities in Oman. To complement the explanatory analysis, multiple machine learning (ML) algorithms were employed to assess predictive performance and variable importance. The results indicate that DKS significantly enhances UIC and serves as a central mechanism linking AII, BCT, and OR to collaboration outcomes, while DIQ exerts a direct but non-mediated effect. GPS further strengthens the relationship between DKS and UIC, underscoring the role of institutional support. ML findings corroborate these results, identifying DKS as the most influential predictor across models. These findings advance UIC research by unpacking the mechanism through which digital transformation influences collaboration and demonstrate the value of integrating SEM with ML to provide complementary explanatory and predictive insights, offering practical implications for policymakers and managers. The study also contributes to the SDGs by enhancing inclusive economic growth through quality education, innovation, and digital capabilities.