“Would you not prefer to ride a vehicle with the smart inbuilt system which scans all the component of your vehicle and predict any fault or failure in real time and give you a smooth driving experience?” Despite some serious advancement in vehicle system including an intelligent infrastructure and many different interactive driver assistance system, there are still some major drawbacks which needs to be resolved efficiently and effectual. Generally, drivers expect a smooth ride but during any component failure the whole vehicle system is affected which in turn affects the riding experience of driver. Every machinery regardless of how good manufacturing is done, eventually gets affected directly or indirectly by the surrounding and leads to complications in the system. The main problem domain in the existing vehicle system is inapt detection of fault and prediction of adverse actions caused by these faults. The improper functioning of vehicle system can only be detected if the driver experiences it explicitly. To find these problems user needs to visit a vehicle service centre for identifying the issue. This process is not user friendly because of the inordinate time and extravagant human effort. Another problem discerned by us was that immediate help is not provided to resulting in decreased chances of faster recovery. The problem with the existing system is the need to communicate with emergency call center explicitly. “Now, envisage a smart system which monitors various car systems, predicts the system’s behaviour and failure. A system is capable of detecting scale of damage done during accident and also capable of providing immediate help to victim without any human interaction?” For such a smart system, detection of fault and prediction of action caused by these faults is very crucial. Thus, by the use of Internet of Things(Iot) and Machine Learning(ML) a digital twin of existing system can be devised which could be integrated in any vehicle. For making a strong communication with user, the concept of “Digital Twin” is introduced in this solution which is better than any 3D model. Digital Twin of automobile will be created simulated by sensors data and real time decisions for better system monitoring experience and ease in understanding the component of system which is affected. After our machine learning algorithm identifies the defect in any component/system of vehicle, respective simulations will be sent to digital twin which will then be reflected on LCD (User Interface).