This thesis delves into the bearing systems of existing bridges to enhance monitoring techniques. It covers theoretical and practical aspects, focusing on the types, maintenance, and damage analysis of bridge bearings. Extensive data collection and analysis demonstrate common challenges and propose solutions using advanced technologies such as computer vision and deep learning algorithms. Bearings allow controlled movements like expansion, contraction, and rotation due to various forces acting on the bridge. These components enable different bridge parts to work together, resisting various forces while maintaining alignment. Over time, bearings wear out due to environmental factors like moisture, temperature changes, and chemical reactions, as well as mechanical fatigue from traffic loads.