This research uses machine learning to predict trauma demand and optimise hospital scheduling. By forecasting patient volume and dynamically allocating operating rooms, it reduces cancellations, improves efficiency, and lowers costs. The system has the potential to transform healthcare delivery by balancing emergency and elective care more effectively.

This research explores next-generation digital twins—virtual representations of real-world systems that support decision-making through simulation and AI. By combining decentralization, privacy-preserving architectures, explainable AI, and scenario analysis, the work aims to help individuals and organizations evaluate alternative futures, make informed decisions, and build more transparent intelligent systems.