With the growing demand for automated systems in the automotive industry, car damage detection has emerged as a critical area where machine learning and computer vision can provide substantial benefits. This paper explores the development and deployment of a web-based automated car damage detection system utilizing the VGG16 convolutional neural network (CNN) architecture. The proposed system is designed to analyze car images and detect various types of damages, such as dents, scratches, and cracks. Leveraging the power of machine learning and deep learning models, VGG16 is employed for feature extraction, while the YOLO (You Only Look Once) algorithm is used for real-time damage localization. The entire application is built using Keras as the deep learning framework, Django as the backend web framework, and JavaScript, AJAX, and Python for front-end integration and communication between client and server. This system enables users to upload images of damaged vehicles through a web interface, and the backend processes the images to detect and classify the extent of damage automatically. The application’s performance and scalability make it a practical solution for insurance companies, car service centers, and individuals seeking quick, accurate damage assessment.