Digital twin technology has emerged as a transformative approach to asset integrity management, offering unprecedented capabilities for real-time monitoring, simulation, and predictive analytics. This paper presents a comprehensive exploration of digital twin technology as a novel and effective method for corrosion monitoring in industrial pipeline systems. A digital twin, in this context, refers to a dynamic, virtual representation of a physical pipeline that is continuously updated with operational and environmental data via embedded sensors and networked systems. The study discusses the foundational elements of digital twins, including the integration of sensor-derived data streams, physics-based corrosion models, and machine learning algorithms to replicate and forecast corrosion behavior over time. Unlike traditional corrosion monitoring methods—which are typically reactive and periodic—digital twins enable continuous surveillance, allowing for early detection of degradation, precise risk assessments, and data-driven maintenance planning. This predictive capability enhances operational efficiency, extends asset life, and reduces the likelihood of catastrophic failure. Key advantages highlighted include real-time visualization of corrosion rates, automated anomaly detection, and the ability to conduct virtual testing under various environmental and operational conditions. The paper also examines implementation challenges such as data integration complexity, model calibration accuracy, cybersecurity considerations, and the high initial cost of deployment. Through selected case studies and pilot deployments, the research illustrates how digital twin frameworks are reshaping pipeline corrosion monitoring strategies. Recommendations are provided for integrating digital twins into existing corrosion management programs, emphasizing the need for cross-disciplinary collaboration and scalable digital infrastructure. Overall, the study underscores the value of digital twins in achieving more resilient, intelligent, and sustainable pipeline systems.