Road accidents are caused by multiple factors including vehicular conditions. Improper upkeep of old vehicles harms the environment and poses safety risks. This research work aims to present a deep learning-based approach for the testing of the fitness of the vehicles physically as well as verifying the validity of its documents. This has also become a major challenge because of the multiple restrictions and lockdowns throughout the country. As per the guidelines of the Government of India, every vehicle needs to undergo a mandatory fitness test in which the examiner will validate all the documents like Pollution Under Control (PUC) certificate, Registration book, Insurance and claims history, etc. Alongside, he will check all the physical parts, looks for any damage in the body or in the working conditions of the instruments, lights, etc. This testing is currently mandatory for the vehicles whose age is above 15 years for private vehicles, and then after every 5 years and for vehicles aged above 2 years for the commercial vehicles. Currently, the whole process of getting the vehicle fitness tested is manual and is quite hassle some for both the owners and the inspectors. This research proposes to automate this process by using three modules in our project. The three projects that includes PUC Validity Check, Automatic Number Plate Recognition (ANPR), and Dent Detection in the vehicles using various artificial intelligence technologies like Computer Vision (CV), Object Detection using models like YOLOv10 and Masked R-CNN, and algorithms like Tesseract and its python version (pytesseract) for performing the Optical Character Recognition (OCR). The project will also come along a front-end web-based application written in Flask, a python-based web framework. Flask framework is considered as simpler than its counterpart Django, as it has less boilerplate code, and is built after taking into considerations and reviews of the python community.