The increasing complexity of offshore energy infrastructure has intensified the need for advanced monitoring and predictive maintenance strategies to ensure the reliability and safety of subsea pipeline systems. Offshore pipelines operate under extreme environmental conditions, including high pressure, corrosion exposure, mechanical stress, and unpredictable ocean dynamics. Traditional inspection and maintenance approaches, which rely heavily on periodic inspections and reactive repair planning, often struggle to provide timely insights into structural degradation and potential failure risks. As offshore energy operations expand into deeper and more challenging marine environments, more intelligent and proactive infrastructure management approaches are required to improve operational reliability and reduce the likelihood of catastrophic failures. Digital twin technology has emerged as a transformative solution for modeling, monitoring, and optimizing complex industrial assets in real time. A digital twin creates a dynamic virtual representation of a physical system by integrating sensor data, operational parameters, and environmental conditions into a computational simulation model. In offshore pipeline systems, digital twin frameworks can continuously analyze structural integrity, simulate degradation processes, and evaluate the effectiveness of different repair and maintenance strategies. By combining high-fidelity simulations with real-time operational data, digital twin platforms enable engineers to predict failure risks, schedule inspections more efficiently, and prioritize intervention strategies based on asset criticality. This study explores the development of digital-twin simulation systems designed to optimize offshore pipeline repair planning, inspection scheduling, and risk-based maintenance strategies for subsea infrastructure. The proposed framework integrates structural monitoring data, environmental variables, and predictive analytics models to simulate pipeline degradation and assess maintenance decision scenarios. Simulation results demonstrate that digital twin–driven maintenance planning can significantly improve inspection efficiency, reduce operational downtime, and enhance subsea asset reliability by enabling proactive and data-driven infrastructure management strategies.