As urban populations continue to surge, the prevalence of traffic-related issues escalates, leading to heightened concerns over public safety, property damage, and various offences posing significant risks to life and assets. Traditional solutions have relied heavily on infrastructure-integrated systems, which are often costly to install and maintain, and lack flexibility and scalability. This study aims to address these challenges by developing a low-cost, real-time vehicular monitoring and reporting system. The system employs readily available technology, built on a foundation of electronic architecture, encompassing a network unit (tunnel server), mobile unit (mobile app), and number plate detection unit. The process involves establishing an HTTP connection between the tunnel server and the mobile app. A tunnelling server, a web application, and a number plate detection unit collaborate to detect license plates in real-time. ML5.js and OpenCV.js are employed to process captured frames, identify objects, and extract license plate numbers. This study marks a significant technological achievement in the realm of web and mobile applications, computer vision, and artificial intelligence. The developed system successfully detects license plate numbers, promising enhanced public safety, property protection, and traffic management. It is recommended that future enhancements, such as expanding its object recognition capabilities and maintaining a robust testing and quality assurance process, should ensure its continued excellence.