This research builds upon previous FRA-funded studies using the LRAIL 3D laser triangulation scanning system and Deep Convolutional Neural Networks (DCNNs) to identify track health attributes. The focus lies on developing technology-agnostic methodologies for processing LRAIL data, catering to diverse end-users in the railway sector. At the component level, a track component health index (TCHI) is introduced, offering a numerical evaluation of track health based on component conditions. Specific indexes for ballast, crossties, and fasteners are combined into a global TCHI, providing a holistic understanding of rail superstructure condition. From a system level, the research also introduces the track strength index (TSI) to numerically assess track buckling resistance. Machine vision-based inspections prove valuable for infrastructure owners, aiding in condition detection and tracking. This approach offers a holistic and quantifiable understanding of track health and strength, enhancing maintenance strategies and overall railway safety. Visualization of data supports decision-makers in prioritizing maintenance, potentially reducing the risk of track-caused derailments.