Environment with green practices depends heavily on efficient waste management, especially in parts of the economy where garbage is produced and processed in huge quantity Plastic bottles are one of the largest impediments to waste material due to their bulk use and destructive effect on the environment Inability of conventional methods of garbage segregation to offer the required precision and speed to function in most situations renders garbage segregation ineffective. To counter the issue, in this project, an improved waste sorting mechanism using the YOLOv8 object detection algorithm is utilized. Image processing and real-time machine learning are utilized by the system to separate and filter plastic bottles from the rest of the waste materials on a conveyor belt accurately. On grounds of efficiency and effectiveness, the YOLOv8 algorithm is superior to traditional methods and previous models. It is also highly renowned for detecting objects with a very high degree of accuracy. Other methods did not extend beyond detection, but auto-sorting ensures that plastic trash is sorted according to what needs to be recycled. This raises the overall processes of waste management and lowers contamination. Because it can provide businesses with a more and more scalable option, this invention is a giant leap for automated waste management.