## 1 Introduction  
In recent years, the demand for high-quality wireless connectivity has steadily increased. This increasing demand is a result of emerging applications and services, such as autonomous vehicles, IoT devices, online video streaming, and augmented reality, that are integrated into the multiverse of NFTs [1, 2]. This massive demand leads to an increase in data traffic and causes problems for the mobile network infrastructure. Since 2011, data traffic on mobile networks has increased 300-fold, growing 42% per year. By the end of 2027, the total volume is predicted to reach 370 exabytes [3]. The prediction shows a rapid acceleration in the massive use of IoT in smart cities, with 51% of all connections in the mobile network by 2027, leading to relevant discussions on the adoption of the IoE concept, which is gaining traction along with fifth- and sixth-generation networks [3, 4]. The Internet of Things (IoT) is the fastest growing category, expected to generate massive network traffic and potentially account for half of global connectivity. Within this segment, home automation apps will have the largest share at 48% of devices, and autonomous cars will see the largest growth over time [5]. This accelerated growth has a direct impact on telecommunication networks, causing congestion that affects the quality of service and experience if there is no planning. Therefore, determining the traffic on the network is extremely important to allow for planning and to support the network at critical moments. For temporary events such as concerts and sporting events, it is easier to calculate and predict the instantaneous traffic than in situations where the mobile network load fluctuates and depends on time and space or due to an environmental disaster [6]. In this context, the use of unmanned aerial vehicles (UAVs) mounted on base stations (UAV-BS) can serve to complete the existing mobile network structure [7], provide wireless communication services to blocked users, increase the capacity of the mobile system [8], minimize coverage gaps, and help reduce the sudden increase in demand that is one of the main problems leading to congestion during hotspot events [9]. Unlike fixed infrastructures, UAVs can provide time-limited services on demand due to their flexibility, rapid deployment without constraints, and low cost compared to traditional services [10; 11]. UAVs can be used to bring communications to areas where there is no infrastructure and as network backup to improve fault tolerance [12]. In areas of environmental disaster, UAVs can be used as the primary equipment to support rescue and as a communication tool to expedite self-rescue [13]. Standardization activities are already underway, regulated by 3 GPP, to provide promising services using UAV BSs for the mobile network [14]. In addition, integrating UAVs with the next-generation 5G mobile system can be an excellent solution for users and applications that rely on low latency and high throughput, improving the quality of experience (QoE) in the region [15]. The combination of these two technologies can be beneficial for telecommunication networks. For example, the low consumption of 5G operations can help with the power shortage that exists on the UAV side, and networks can become more secure through the use of encryption in 5G. UAVs can help in the deployment of 5G in regions where network deployment will be gradual or is non-existent, and in planning in regions where there are only previous generation networks that face congestion issues [16]. The use of UAVs to support next-generation 5G/6G networks is extremely important to improve the quality of the mobile system by increasing the coverage area and providing high-quality Internet in regions where the mobile network is poor. In this paper, we propose an intelligent system based on machine learning using UAV-BS as a base station to support the mobile communication network, which is affected by the impact of the congestion caused by the high density of users.