Abstract— This paper presents an Unmanned Aerial Vehicle (UAV) and Tethered Balloons (TBs) placement optimization framework that adapts to real-time traffic variations in wireless networks. The optimization problem aims to maximize end-to-end network throughput and minimize service latency by dynamically positioning UAVs and TBs. The proposed approach employs heuristic algorithms – namely Recursive Random Search (RRS) and a Shrink-and-Realign (S&R) process – to optimize UAV and TB positioning. The obtained simulation results unveil that the dynamic placement strategy achieves a maximum throughput of approximately 800 Mbps at 30 dBm UAV transmit power, outperforming static and random placement strategies, which reach 700 Mbps and 600 Mbps, respectively. Moreover, the proposed strategy reduces service latency to 15.3 ms, marking a 30% improvement over the static placement method (21.8 ms) and a 34.6% improvement over the random approach (23.4 ms). The algorithm exhibits rapid convergence, typically stabilizing within 10 iterations, ensuring its practical applicability in real-time network environments. On top of that, analysis of user distribution impact confirms that the proposed approach maintains superior performance across varying network conditions. Furthermore, the achieved results validate the effectiveness of the proposed adaptive optimization framework, making it a promising solution for next-generation wireless communication systems.