The rapid expansion of Internet of Things (IoT) deployments across diverse domains has intensified the demand for efficient and sustainable power management solutions. Traditional reliance on disposable batteries presents critical environmental and logistical limitations, necessitating novel alternatives. This research introduces a transformative framework that employs Unmanned Aerial Vehicles (UAVs) to assist a Green Base Station (GBS) equipped with wireless power transfer capabilities to provide on-demand charging for widespread IoT devices. Central to our approach is the integration of Clustering and Routing Approach, which enables UAVs to autonomously learn optimal routing and charging policies in real-time, adapting to dynamic energy demands and environmental conditions. To validate our approach, we conduct extensive simulations comparing the proposed method with conventional clustering-based techniques, including K-Means, Hierarchical, and Greedy algorithms. The results demonstrate superior performance in operational efficiency, scalability, and responsiveness. This study underscores the potential of intelligent, UAV-assisted wireless charging as a sustainable and adaptive energy solution, paving the way for more resilient and autonomous IoT infrastructures.