The study shows that numerous academic researchers are utilizing machine learning and artificial intelligence approaches to regulate, administer, and run networks, as a result of the recent explosion in interest in these fields. In contrast to the scattered and hardware-centric traditional network, Software Defined Networks (SDN) are a linked and adaptive network that offers a full solution for controlling the network quickly and productively. The SDN-provided network-wide information may be used to improve the efficiency of traffic routing in a network environment. Using machine learning techniques to identify the fewest overloaded path for routing traffic in an SDN-enabled network, we investigate and demonstrate their application in this study. These years have seen an increase in the number of researchers working on traffic congestion prediction, particularly in the field of machine learning and artificial intelligence (AI). This study topic has grown significantly in recent years on account of the introduction of large amounts of information from stationary sensors or probing traffic information, as well as the creation of new artificial intelligence models. It is possible to anticipate traffic congestion, and particularly short-term traffic congestion, by analyzing a number of various traffic parameter values. When it comes to anticipating traffic congestion, the majority of the studies rely on historical information. Only a few publications, on the other hand, predicted real-time congestion in traffic. This study presents a comprehensive summary of the current research that has been undertaken using a variety of artificial intelligence approaches, most importantly distinct machine learning methods.