Railroads worldwide are leveraging machine vision technologies to enhance railcar inspection quality and efficiency, ultimately improving railway safety. In collaboration with Canadian Pacific Kansas City (CPKC), the University of Alberta (U of A), TC’s Rail Safety and Security Directorate, and the National Research Council Canada (NRC), Transport Canada’s Innovation Centre (TC) launched the Automated Machine Vision Inspection Systems (AMVIS) project in 2021 to assess the capabilities of remotely monitored train inspection technologies. This project studied the reliability of the Train Inspection Portal System (TIPS) under various climatic conditions, the effectiveness of Portal Office Inspection (POI) in detecting safety defects, and the potential of AI algorithms to support inspectors. The results provide evidence that TIPS enables real-time, high-quality imaging without disrupting train operations, reduce idling time and improve defect detection for several defect types. Additionally, AI models such as YOLOv5 and Faster R-CNN demonstrate strong potential in automating defect identification, particularly for wheels and cap screws. Key recommendations include optimizing camera placement for enhanced imaging, refining POI software for improved defect detection, and integrating AI-driven solutions to elevate inspection accuracy and efficiency.