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Recent years have seen a surge in the use of technology for executing transactions in both online and offline modes. Various industries like banking, e-commerce, and private organizations use networks for the exchange of confidential information and resources. Network security is thus of utmost importance, with the expectation of effective and efficient analysis of the network traffic. Wireless Mesh Networks are effective in communicating information over a vast span with minimal costs. A network is evaluated based on its security, accessibility, and extent of interoperability. Artificial Intelligence techniques like machine learning and deep learning have found widespread use to solve a range of challenging, real-world problems. These techniques are well known for their ability to detect issues or patterns in traffic along with advancements in computing capabilities. Extensive research is being carried out to improve the performance of Wireless Mesh Networks. This survey aims to provide a disinterested overview of the application of different artificial intelligence techniques to enhance network performance. We focus on approaches that address the three fundamental problems in networking: traffic prediction, traffic routing, and congestion control. Our paper also includes the bibliometric analysis of the literature, highlighting the ongoing efforts in terms of statistics across multiple metrics. This survey aims to provide researchers in this community with a reliable compendium to get a brief yet succinct understanding of the current progress in the domain.