###### Abstract
[10] propose a proactive radio-QoS-aware unmanned aerial vehicle ground station (UAV-GS) deployment mechanism to improve the cellular network operation mechanism. The proactive QoS-aware deployment strategy could optimize UAV-GS positioning based on LTE network conditions and QoS requirements. Conventional bandwidth allocation methods used in 5G-enabled UAV communication systems, such as proportional fairness, max-min fairness, and zero-forcing-based scheduling, are often constrained by static allocation rules, rigid traffic handling mechanisms, and assumptions of fixed network topologies. These limitations become particularly problematic in dynamic scenarios involving UAV mobility and diverse application demands (e.g., voice, video, and data). Furthermore, most of these traditional approaches lack predictive capabilities to anticipate bandwidth requirements under fluctuating latency and signal conditions. To address these challenges, this study proposes a machine learning-based simulation framework that employs least square gradient boosting (LSGB) and a Bayesian regularization feedforward neural network (BRFFNN) to enhance bandwidth allocation efficiency between UAVs and ground stations in a 5G network. The framework simulates communication under two distinct UAV movement patterns, random and circular, using a real-world 5G traffic dataset. The effectiveness of each model is evaluated through key QoS metrics, including the packet delivery ratio (PDR), delay, and throughput, to determine their suitability for real-time bandwidth optimization. For UAV-enabled mission-critical applications like public safety, emergency response, and disaster management, dependable and low-latency communication is crucial. According to recent research, 5G network slicing improves mission-critical communications by offering Public Protection and Disaster Relief (PPDR) channels that are isolated, incredibly dependable, and low latency [10]. Similarly, cloud-native 5G architectures have been proposed for managing mission-critical services with stringent QoS requirements, including capacity scaling, service migration, and edge deployments [11]. It has been demonstrated that device-to-device (D2D) communication under 5G enhances network resilience and facilitates effective disaster response in disaster scenarios [12]. Furthermore, the 5G-EPICENTRE project has highlighted the role of cloud-native infrastructures and network applications (NetApps) in enhancing disaster relief operations and public safety [13]. This study focuses on the basic problem of predicting bandwidth requirements under various movements and traffic types in order to overcome the drawbacks of static bandwidth allocation in dynamic UAV environments. When UAV movement is unreliable, conventional methods frequently lack adaptability and fail to maintain QoS. Although earlier research has looked at heuristic or optimization-based approaches, there is still a technical barrier to using machine learning models to dynamically estimate bandwidth based on real-time communication features. In response, we use a 5G dataset to propose and assess two ML-based models, LSGB and the BRFFNN, under both random and circular UAV trajectories. Comparative analysis of these models in forecasting bandwidth allocation and enhancing QoS outcomes, specifically the PDR, throughput, and delay, is our main contribution.