Broadband control in wireless networks has become crucial due to the growing demand for wireless connections and the proliferation of Internet of Things (IoT) devices. Setting static settings for network factors, including routing protocols, packet sizes, and modulation techniques, is the conventional bandwidth management method. Nevertheless, poor network performance and wasteful resource use might result from these static arrangements. Artificial intelligence (AI) algorithms have become a viable option for wireless network dynamic bandwidth control in recent years. This paper aims to investigate how AI algorithms may be used to control wireless network capacity. They are creating and assessing artificial intelligence (AI) systems that can forecast traffic patterns and dynamically change network settings to maximize efficiency.