In a world grappling with escalating environmental concerns, effective disposal of trash plays a pivotal role. According to World Bank reports, four billion tonnes of waste is produced annually and Nine Thousand tonnes daily thus calling for effective solution of classification. This work needs to be automated as human segregation of trash is hazardous to health and requires a lot of time. If the trash is segregated properly, it facilitates the process of reuse and recycle and helps in proper disposal of unnecessary materials thus helping prevent harmful gases like methane into the environment. Previous works in this domain have been considerate with the solution but has not been able to cover variety of classes of Trash. We,in this research work would like to infuse TrashBox dataset, a comprehensive collection of images of waste categories divided into seven classes with the object detection models like YOLO towards automating trash detection and precise classification that helps in achieving better waste sorting accuracy. The research work core ambition is to exploit distinctive attributes of YOLO models combined with Arduino to give rise to a more effective trash sorting system. Traditional waste management processes rely heavily on manual labor, making them time-consuming, costly, and prone to human error. To overcome these limitations, we propose the development of a deep learning-based model that can swiftly detect and classify different types of waste items in real-time. YOLO, a state-of-the-art object detection algorithm, will serve as the backbone of our model, ensuring fast and accurate identification of trash items. The integration of an Arduino microcontroller into our system adds a practical and interactive dimension. Arduino will facilitate seamless communication between the deep learning model and the physical world. This enables our system to trigger actions such as sorting, recycling, or alerting authorities, depending on the detected trash item and its classification.