The transition to sustainable energy has intensified the focus on hydrogen as a clean fuel alternative due to its potential for significant greenhouse gas emission reductions. However, hydrogen’s safe and efficient transportation presents considerable challenges, primarily because of its broad flammability range and low activation energy, which increase the risk of leaks and associated hazards. Timely detection and swift response to leaks are essential for mitigating threats to the environment, infrastructure, and public safety. To address these challenges, we are developing a digital twin-based pipeline risk analysis model that leverages environmental data, including weather forecasts and geographic information system (GIS) data, to accurately identify high-risk segments along hydrogen pipelines. This model will support rapid deployment of emergency services and repair teams to targeted locations and assist operations personnel in pinpointing areas requiring proactive maintenance, ultimately enhancing the safety and reliability of hydrogen transport networks. In this paper, we report on the development of an agent-based model that forms the core of this digital twin.