Traditional approaches to pipeline condition assessment often rely on periodic inspections and conventional analysis, which is time-consuming, costly, and prone to human error. However, with the advancements in data analytics and the increasing availability of large volumes of data, adopting Big Data Analytics for energy pipeline condition assessment has emerged as a promising solution. By leveraging the massive volumes of data generated by sensors, meters, and other monitoring devices installed on these pipelines.