Railway transportation requires constant inspections and immediate maintenance to ensure public safety. Traditional manual inspections are not only time consuming, but also expensive. In addition, the accuracy of defect detection is also subjected to human expertise and efficiency at the time of inspection. Computing and Robotics offer automated IoT based solutions where robots could be deployed on rail-tracks and hard to reach areas, and controlled from control rooms to provide faster and low-cost inspection. In this thesis, a novel automated system based on robotics and visual inspection is proposed. The system provides local image processing while inspecting and cloud storage of information that consist of images of the defected railway tracks only. The proposed system utilizes state of the art Machine Learning system and applies it on the images obtained from the tracks in order to classify them as normal or suspicious. Such locations are then marked and more careful inspection can be performed by a dedicated operator with very few locations to inspect (as opposed to the full track).