This paper discusses a new way to maintain cars using artificial intelligence. It describes a system that helps cars check for themselves, send reports to drivers, and instructs how to fix issues. The Engine UI (User Interface) Predictor uses machine learning with an Arduino setup, which collects data from sensors about the engine and checks how the engine is performing. Collect real-time details such as engine RPM, fuel pressure, coolant pressure, lubricant temperature, and coolant temperature and transfer them to the model. The model provides real-time data in an interval of ten seconds to the Engine UI. The model uses these data to predict performance and sends alerts to the owner or driver regularly. If it finds a problem, the system gives helpful advice with easy steps for simple maintenance tasks. It also creates detailed reports for mechanics that show what is wrong and how to fix it. This new method aims to make cars more reliable, reduce maintenance costs, and improve safety. The Gradient Boosting model achieves 66.60% accuracy compared to other machine learning models such as Random Forest and Support Vector Machine. This technology offers better comfort, reduces maintenance cost and greater safety, showing how the ML model can make cars smarter and safer.