Waste classification is an important step in the waste management process, as it helps identify the types of waste and how they should be handled. Traditional waste classification methods are typically manual and time-consuming, which can result in errors and inconsistencies. With the increasing amount of waste being generated globally, there is a need for more efficient and accurate methods for waste classification. Machine learning techniques, such as deep learning algorithms, have shown promising results in automating waste classification. Among these algorithms, the VGG architecture has been widely used for image classification tasks and has achieved state-of-the-art performance on several benchmarks. The VGG architecture consists of several convolutional layers and pooling layers, followed by several fully connected layers, and has the ability to learn complex image features. In this project, we propose a method for smart wastage classification using the VGG (Visual Geometry Group) algorithm. The proposed method involves training a deep convolutional neural network (CNN) based on the VGG architecture to classify waste images into different categories, such as paper, plastic, glass, metal, and organic. The CNN model is trained on a large dataset of waste images, which is pre-processed and augmented to improve the model's accuracy. The proposed method is evaluated on a test dataset and compared with other state-of the-art methods, demonstrating its effectiveness in smart wastage classification. The results indicate that the proposed method can accurately classify waste images, which can help improve waste management practices and reduce environmental pollution.