###### Abstract
Analyzing traffic patterns to decrease road accidents and optimizing sailing routes to cut emissions are two examples of how artificial intelligence (AI) opens up the choices for making transportation safer, more dependable, more efficient, and cleaner [6]. This technology helps in the obtaining of pertinent information on their effects and of new measures to illustrate this procedure and validate the benefits of new technologies in transportation [7]. According to results from previous studies, incident detection algorithms can be divided into five main categories based on how they analyze data throughout their operations. They are the following: comparative algorithms, statistical algorithms, time series- and filtering-based algorithms, and traffic theory-based algorithms [8; 9]. Traffic issues such as parking, speeding, and congestion are examined based on the spatiotemporal map's dispersion and the creation state of the trajectory. According to experimental findings, the proposed method significantly speeds up vehicle detection and tracking and produces high MAP, MOTA, and MOTP indicators [10]. This paper presents an overview of incident detector applications in road transportation systems, utilizing machine learning and artificial intelligence. Artificial intelligence and machine learning play a significant role in predicting road system incidents. The study aims to address the constant cause of road accidents and the application of AI and ML to curb the cause of this growing menace. The primary motivations of this study are listed below:  
* To better understand the various applications of artificial intelligence and machine learning as related to the incident detectors in the road system. * Despite various research concerns with incident detectors by researchers, various challenges and concerns about road transport systems still need to be investigated. * The need to study the impacts of 5G and emerging 6G technologies on road transport systems is of the utmost importance. * A wide range of wireless communication with the internet of vehicles (IOV) and different underlying networks, such as real-time vehicle tracking, traffic management, and other networks, needs to be investigated in order to be properly deployed for effective road management. The significance of artificial intelligence and machine learning to evaluate the incident detector by applying it to road systems is crucial to avoid death and injuries. Therefore,conducting this study to address the issue is of the utmost importance as it will enhance the knowledge of the underlying mechanisms in the application AI and ML and will not forget the challenges that may occur. The layout of the paper is given in Figure 1.  
The contribution of this paper is as follows:  
* The study contributed by extensively reviewing the literature to search for the effect of ML and AI on the incident detector in road transport systems. * The study investigates the key areas of the usability of artificial intelligence and machine learning in road transport systems, such as the internet of vehicles, the ad hoc vehicle network, and wireless communication with the internet of vehicles, 5 th generation and 6 th generation. * The study examines real-time vehicle tracking, which is vital in incident detector systems in road systems. This paper further summarizes the challenges facing the application of artificial intelligence in road transport systems. The remaining sections of this article are structured as follows: Section 1 presents the study's introduction, Section 2 presents an overview of the artificial intelligence and machine learning in the road transport system, and Section 3 provides examples of how these technologies are used in actual road transport systems. We outline current trends, potential paths for future research, and key takeaways in Section 4. Finally, Section 5 provides the conclusion of the paper.