<p>The IoT networks consists of sensor nodes which are battery-powered microsystems equipped with transducers to monitor the environment and to communicate with each other. Consequently, the IoT produces massive amounts of digital data, which require fast processing and analysis. Artificial intelligence (AI) is widely employed to solve complex scientific, technical, and practical problems. Such AI techniques as Neural Networks, Fuzzy Systems, Genetic and Evolutionary Algorithms are commonly employed in wireless networks to promote their optimization, prediction, and management. The AI approach provides optimized results in a challenging task of call admission control, routing, handover, and traffic prediction in wireless networks. Call admission control plays a significant role in providing the desired quality of service, and an effective call admission control algorithm is needed to optimize wireless networks of IoT. Numerous call admission control schemes have been proposed. The paper presents a methodology for creating a genetic neuro-fuzzy controller for call admission in 5G networks of IoT. The performance of the proposed admission control scheme is assessed through computer simulation.</p>