ABSTRACT Road safety is a critical issue due to its significant impact on public health and economic stability. Traffic accidents result in millions of fatalities and injuries globally each year, imposing substantial healthcare costs and loss of productivity. Therefore, systematic data collection is urgently needed to identify key road safety challenges and implement effective solutions. This study examines recent advancements in artificial intelligence (AI) and deep learning techniques for detecting road anomalies, including potholes and speed bumps, utilizing cost‐effective, commercially available cameras. It provides a comprehensive overview of various methodologies for detecting road damage, emphasizing the value of integrating visual, qualitative, and quantitative analyses. Additionally, the study evaluates various algorithms, including R‐CNN (Regions with CNN) for object detection and CrackU‐net for crack detection, to analyze their effectiveness in enhancing road maintenance and safety. Beyond technical methods, the study also examines global trends in road safety, emphasizing the need for comprehensive policy frameworks and knowledge transfer from developed to developing countries to reduce fatalities and enhance road infrastructure. Finally, the study addresses challenges such as limited visibility, adverse weather conditions, and the current limitations of existing models, while discussing the potential for future advancements in automated road safety systems. This article is categorized under: Technologies > Artificial Intelligence