Railway Overhead Catenary (ROC) system is critical for railways’ overall performance! ROC is a linear asset that is spread over a large geographical area. Insufficient performance of ROC has a significant impact on the overall railway operations, which leads to decreased availability and affects performance of the railway system. Prognostic and Health Management (PHM) of ROC is necessary to improve the dependability of the railway. PHM of ROC can be enhanced by implementing a data-driven approach. A data-driven approach to PHM is highly dependent on the availability and accessibility of data, data acquisition, processing and decision-support. Acquiring data for PHM of ROC can be used through various methods, such as manual inspections. Manual inspection of ROC is a time-consuming and costly method to assess the health of the ROC. Another approach for assessing the health of ROC is through condition monitoring using 3D scanning of ROC utilising LiDAR technology.Presently, 3D scanning systems like LiDAR scanners present new avenues for data acquisition of such physical assets. Large amounts of data can be collected from aerial, on-ground, and subterranean environments. Handling and processing this large amount of data require addressing multiple challenges like data collection, processing algorithms, information extraction, information representation, and decision support tools. Current approaches concentrate more on data processing but lack the maturity to support the end-to-end process. Hence, this paper investigates the requirements and proposes an architecture for a data-to-decision approach to PHM of ROC based on utilisation of LiDAR technology.