The Nigerian oil and gas industry relies heavily on extensive pipeline networks for the transportation of crude oil, natural gas, and refined products. However, these pipelines are frequently threatened by corrosion, leakages, vandalism, and operational inefficiencies, leading to substantial economic losses and environmental degradation. In recent years, the integration of Artificial Intelligence (AI) technologies has emerged as a transformative approach to improving pipeline monitoring, maintenance, and overall system reliability. This paper explores the application of AI-driven tools, such as machine learning algorithms, computer vision, and predictive analytics in enhancing real-time monitoring, early fault detection, and proactive maintenance of oil and gas pipelines in Nigeria. By analysing case studies and global best practices, the study highlights how AI can optimize inspection schedules, reduce downtime, and minimize environmental risks. Furthermore, the paper discusses the challenges limiting widespread adoption in Nigeria, including data scarcity, infrastructure deficits, and skill gaps, while proposing strategic frameworks for sustainable implementation. The findings emphasize that leveraging AI in pipeline operations not only strengthens asset integrity and operational efficiency but also advances Nigeria’s transition toward a safer, more sustainable, and digitally resilient energy future.