Traffic offenses pose a significant challenge to road safety, particularly in developing countries like Nigeria, where the consequences often result in severe accidents and fatalities. This paper surveys recent advancements in artificial intelligence (AI) and deep learning methodologies applied to traffic offense prediction. By reviewing various studies, we examine the effectiveness of different models, including multidimensional data analysis, computer vision, and neural networks, in identifying and predicting traffic violations. The literature highlights the importance of integrating diverse data sources and local context to enhance the accuracy of predictive systems. Despite notable progress, significant gaps remain, especially in region-specific applications that consider unique traffic dynamics. Our findings underscore the need for further research to develop robust, context-aware AI solutions that can effectively mitigate traffic offenses and improve overall road safety. This survey aims to provide a comprehensive overview of existing approaches while laying the groundwork for future innovations in traffic management systems.