The automotive industry's rapid growth has led to increased vehicle numbers and subsequently higher failure rates. Conventional diagnostics react to failures, lacking preventive capabilities. Current On-Board Diagnostics are no longer sufficient, necessitating upgrades. To address this, I propose implementing Internet of Things (IoT) devices and Deep Learning Models to predict failures in advance, saving costs and avoiding mishaps. These models utilize historical data and vehicle tests to establish threshold values, triggering warnings if the system detects potential failures. The system comprises an OBD device sending data to a remote server, which updates a dashboard with real-time failure alerts.