###### Abstract  
Efficient bandwidth allocation in 5G networks is essential for optimizing network performance and ensuring high quality of service (QoS), particularly in unmanned aerial vehicle (UAV) communication systems. The dynamic nature of UAV networks presents challenges in managing fluctuating QoS levels, necessitating intelligent bandwidth allocation strategies. This study investigates the effectiveness of two machine learning (ML) models, least square gradient boosting (LSGB) and a Bayesian regularization feedforward neural network (BRFFNN), in predicting bandwidth allocation for UAV ground station (UAV-GS) communication under 5G specifications. Using a simulation-based approach, the study evaluates UAV bandwidth allocation under two movement patterns: circular and random. The QoS metrics considered include the packet delivery ratio (PDR), delay, and throughput. The results demonstrate that the BRFFNN outperforms LSGB, particularly in circular UAV movement, achieving a 100% PDR, a 0.00773 ms delay, and a 3.232 million packets per second (pps) throughput. These findings suggest that ML models, particularly the BRFFNN, can significantly enhance bandwidth allocation strategies in 5G UAV-GS communication systems, improving overall network efficiency and QoS. This study provides valuable insights into ML-driven bandwidth allocation, emphasizing the BRFFNN as a superior approach for enhancing QoS in 5G UAV-GS networks. In the context of 5G UAV-GS bandwidth allocation, this study applies the BRFFNN in a novel way and demonstrates its superiority over tree-based models such as LSGB. In contrast to earlier research that concentrated on static or traditional allocation techniques, our method achieves State-of-the-Art QoS by dynamically predicting bandwidth under actual UAV movement scenarios. the past decade due to their flexibility in addressing these communication demands. UAVs have been explored as a means to facilitate the deployment of wireless communication services in public spaces. They are proposed to enhance wireless coverage, increase capacity, improve reliability in existing infrastructure, and even replace damaged equipment during emergencies. Equipped with transmission electronics, UAVs have been suggested as portable base stations or access points. To provide communication services to ground users, the deployment of a UAV-to-ground access network is typically required if such infrastructure is not already in place [4]. Multipath transmission enhances network performance considerably, but introduces complexities in ensuring equitable allocation. Traditional bandwidth allocation strategies, designed for single-path scenarios, often prove inadequate in multi-path contexts, underscoring the challenges of achieving fair bandwidth distribution in such networks [5]. In [6], the bandwidth allocation and UAV placement are optimized separately. The UAV placement problem, involving complex sum-of-ratio functions, is addressed using a difference of convex (DC) optimization and quadratic transformation to iteratively convexify and solve it. The results highlight the impact of various parameters on system throughput and demonstrate significant gains compared to baseline methods. In [7], two bandwidth allocation schemes were proposed for improving fairness and link utilization in data center networks. These include a no-reallocation scheme that ensures fair transmission rates and a reallocation scheme that redistributes unused bandwidth to competing flows. In their work, there are two client groups, A and B, and these groups have access links with different delay times. [PERSON] et al. [8] proposed a dynamic bandwidth allocation strategy for networked control systems, ensuring that each loop's bandwidth stays within bounds and that the total allocation remains within capacity. The strategy involves estimating errors, designing a desired bandwidth algorithm, calculating dynamic weights, and presenting an ideal allocation strategy, with simulations confirming its effectiveness [9]. [PERSON] et al.