The increasing volume of waste generated worldwide presents significant environmental challenges, necessitating efficient and automated waste management solutions. This paper explores the application of artificial intelligence (AI) in waste sorting and recycling, focusing on the implementation of the Faster R-CNN (Region-based Convolutional Neural Network) model for object detection. Faster R-CNN is renowned for its accuracy and speed in detecting and classifying objects within images, making it an ideal candidate for real-time waste segregation tasks.Our research involves training the Faster R-CNN model on a diverse dataset comprising various waste categories, including plastics, metals, paper, and organic waste. The model's performance is evaluated based on metrics such as precision, recall, and mean Average Precision (mAP). The experimental results demonstrate the model's high accuracy in distinguishing between different types of waste materials, thereby facilitating effective sorting processes.