Road infrastructure is critical to a nation's prosperity and safety. However, the presence of potholes in road networks creates substantial issues to road users, resulting to an increase in accidents and costly vehicle damages. Real-time pothole anomaly detection systems have emerged as a possible solution to this problem, employing innovative technology for fast identification and notifications about pothole existence. In underdeveloped countries with limited road maintenance resources, such technologies have the potential to improve road safety while lowering maintenance costs. This research provides a comprehensive assessment of various implementation options for real-time pothole anomaly detection in developing countries. It investigates the many strategies and technologies that can be used in developing countries to detect anomalies in the road network. The utilisation of deep learning, computer vision, and lidar-based systems is highlighted in particular. Furthermore, the paper addresses the obstacles associated with the deployment of such systems and provides alternative solutions. Additionally, the paper compares the different alternatives, discussing their potential benefits and drawbacks. The findings of the literature analysis and practical evidence reveal that, while deep learning and computer vision-based algorithms produce the most accurate results, their application is limited due to computational and economical constraints in developing countries such as Nigeria. On the contrary, lidar-based solutions offer a realistic and cost-effective alternative to deep learning and computer vision-based systems. Thus, lidar-based pothole detection technologies can be used efficiently to achieve safer roadways in underdeveloped countries such as Nigeria.