###### Abstract  
The Internet of Things (IoT) and wireless sensor networks (WSNs) have evolved rapidly due to technological breakthroughs. WSNs generate high traffic due to the growing number of sensor nodes. Congestion is one of several problems caused by the huge amount of data in WSNs. When wireless network resources are limited and IoT devices require more and more resources, congestion occurs in extremely dense WSN-based IoT networks. Reduced throughput, reduced network capacity, and reduced energy efficiency within WSNs are all effects of congestion. These consequences eventually lead to network outages due to underutilized network resources, increased network operating costs, and significantly degraded quality of service (QoS). Therefore, it is critical to deal with congestion in WSN-based IoT networks. Researchers have developed a number of approaches to address this problem, with new solutions based on artificial intelligence (AI) standing out. This research examines how new AI-based algorithms contribute to congestion mitigation in WSN-based IoT networks and the various congestion mitigation strategies that have helped reduce congestion. This study also highlights the limitations of AI-based solutions, including where and why they are used in WSNs, and a comparative study of the current literature that makes this study novel. The study concludes with a discussion of its significance and potential future study topics. The topic of congestion reduction in ultra-dense WSN-based IoT networks, as well as the current state of the art and emerging future solutions, demonstrates their significant expertise in reducing WSN congestion. These solutions contribute to network optimization, throughput enhancement, quality of service improvement, network capacity expansion, and overall WSN efficiency improvement. WSNs; congestion mitigation; artificial intelligence; game theory; IoT; QoS Artificial Intelligence-Based Solutions  
[PERSON]  
WSNs are interdisciplinary technologies that combine the comprehensive techniques of wireless communications, pervasive computing, networking, and signal processing [6], WSNs have evolved continuously and incrementally since the 1960s [6]. However, the Defense Advanced Research Projects Agency (DARPA) launched a brand-new initiative called Distributed Sensor Networks (DSNs) in the 1970s [7]. The development of DSNs had a positive impact on academic study and scientific research. In the last 50 years, it has also attracted customers and researchers. WSN-based IoT networks consist of widely distributed sensor nodes that allow us to track and respond to events and outcomes at a remote location [8; 9]. The Internet of Things gained the attention of the scientific community in the late 20 th century due to advances in a number of important areas, including communication technology, small hardware, security monitoring, etc. These technological developments made it possible to build low-cost, compact, and multifunctional WSN nodes [10]. Today, the wireless IoT network has evolved into an intelligent, self-healing, extremely dynamic, and distributed system [11]. Due to its efficiency and adaptability, the IoT is currently playing an important role in real-time monitoring and data acquisition. In recent decades, IoT-based WSNs have been developed on a large scale, mainly for heavy industry and military applications. It is often referred to as the Internet of Everything (IoE) due to the widespread use of IoT devices. In this scenario, the IoE offers tremendous potential for the future of the smart world. However, the widespread use of wireless sensor devices will lead to wireless network congestion. When a large number of IoT devices attempt to access network resources that are bandwidth-constrained and lack network traffic management, congestion occurs in communication networks, especially WSNs. This negatively impacts the entire network by reducing network performance and quality of service (QoS), increasing the energy consumption, leading to packet loss and causing security concerns [12].