The detection of surface defects in concrete bridges using deep learning is of significant importance for reducing operational risks, saving maintenance costs, and driving the intelligent transformation of bridge defect detection. In contrast to the subjective and inefficient manual visual inspection, deep learning-based algorithms for concrete defect detection exhibit remarkable advantages, emerging as a focal point in recent research. This paper comprehensively analyzes the research progress of deep learning algorithms in the field of surface defect detection in concrete bridges in recent years. It introduces the early detection methods for surface defects in concrete bridges and the development of deep learning. Subsequently, it provides an overview of deep learning-based concrete bridge surface defect detection research from three aspects:image classification, object detection, and semantic segmentation. The paper summarizes the strengths and weaknesses of existing methods and the challenges they face. Additionally, it analyzes and prospects the development trends of surface defect detection in concrete bridges.