Under its Vision 2030, Saudi Arabia has heavily invested in mega construction projects such as NEOM and QIDDIYA, where logistics resilience is critical due to environmental, infrastructural, and operational uncertainties. This study proposes an IoT-enabled framework for managing project disruptions via real-time sensing, environmental stress indicators, and predictive modeling. Using a dataset of 1,000 logistics events, multiple statistical and machine learning models were applied to evaluate the impact of IoT deployment on delivery delays caused by disruptions, cost variability, and environmental stress. The findings indicate that IoT integration significantly reduces delay volatility and enhances cost predictability. However, disruptions like traffic and weather partially attenuate these benefits. Environmental stress had a minor but consistent influence on logistics risk exposure. A mediation analysis revealed no significant indirect effect of IoT on delay through environmental stress, suggesting a more direct intervention pathway. A decision dashboard is also proposed to visualize delay triggers and automate risk signaling. Theoretical implications extend the Unified Theory of Acceptance and Use of Technology (UTAUT) to construction logistics, emphasizing real-time sensing as the key performance enabler. Managerial implications suggest full IoT adoption in NEOM and QIDDIYA logistics and real-time SCM dashboarding. Future research may incorporate ensemble learning and edge-based IoT to improve predictive capacity. This study demonstrates the feasibility and strategic value of IoT-based disruption control in transforming mega-infrastructure supply chains.