Waste segregation has become one of the crucial tasks which can in turn contribute to efficient recycling of wastes. Manual waste segregation techniques have a lot of issues and also time and efficiency constraints. All these have led to the need for a smart waste segregation system. The main objective is to develop a waste segregation system using a deep learning model built on YOLOv8 architecture, deployed on raspberry pi. The system focuses to classify the wastes to glass, metal, plastic and others by training the model with suitable dataset for each class. The experimental results show that classification model provides high accuracy and fast prediction making it a suitable tool for waste segregation.