With the shift towards hydrogen and renewable energy sources, ensuring safety and operational efficiency in complex, high-risk environments has become paramount [1]. This study introduces a pioneering approach that combines digital twin technology with multiscale mechanics modelling, artificial intelligence, and human factors to create a robust, data-driven framework for safety and risk management in the energy sector. Our digital twin architecture operates across multiple scales, from atomic interactions to full-system simulations, offering a comprehensive view of material behaviour and structural integrity [2]. At the atomic scale, Molecular Dynamics (MD) simulations and AI-driven surrogate models capture hydrogen’s effects on material defects, such as dislocations and grain boundaries, predicting critical phenomena like embrittlement and crack formation. At the mesoscale, simulations focus on dislocation dynamics, crack initiation, and fracture propagation. At the component scale, we assess the durability of infrastructure elements, including pipelines and storage tanks, under high-pressure and cryogenic conditions. Together, these multiscale models empower the digital twin to provide real-time predictive safety insights. A unique aspect of our approach is the integration of human factors within the digital twin. By embedding ergonomic and operational feedback into the model, we enhance the user experience and improve occupational safety [3]. This inclusion of human-centred data addresses noise exposure, equipment fatigue, and limited operational transparency, ensuring the system can anticipate and mitigate hazards in real-time. Our digital twin continuously collects and analyses sensor data, using LiDAR and Bayesian networks for real-time risk assessment and predictive analytics. Case studies on offshore platforms illustrate how the system adapts dynamically to current conditions, allowing for optimised maintenance, hazard monitoring, and risk zone management. This research demonstrates the transformative potential of a digital twin that bridges scales, incorporates human factors, and leverages AI for comprehensive safety management in hydrogen and renewable energy systems. Our findings highlight the digital twin's capability to support safer integration of hydrogen and renewables, contributing to broader safety and sustainability initiatives in the energy sector.