Waste management in urban environments has become more complex and challenging as cities have expanded, primarily due to the increased difficulty in the detection and monitoring of waste generation and disposal. Rapid urbanization leads to a rise in population density, informal settlements, and unplanned infrastructure, all of which contribute to inconsistent waste disposal practices and scattered dumping sites. Our research proposes a solution for garbage detection by integrating drone technologies and image recognition through YOLOv8 model. This methodology employs drones fitted with high-resolution cameras positioned over urban centers to capture images in real time. With GPS and GSM modules, locations are tagged with the images and transmitted to the central system enabling smart waste management. This technology accurately sends the location of trash areas. Using the YOLOv8 processor, images are received and garbage is detected and classified accurately and rapidly. The novelty of this solution is the synergy between aerial photography, YOLOv8, and specialized in-classification systems, which makes it possible to obtain high-resolution repeatable results. Experiments have shown that this system is superior to conventional waste detection system and attains 80–90% accuracy. These methods reduce overhead costs and improve waste management in smart cities, demonstrating the innovative adoption of such technology.