Automotive electronic is full of challenges. Engine controller units, executing millions of line code & performing decision making influenced by several parameters, is one of the most complex embedded device being used. In this presentation we explore the possibility of how machine learning tools can enable the process of making ECU modeling and design validation easier and smarter. A data acquisition system is another essential tool at a race car engineer’s disposal that provides him with data necessary to make valid adjustments to achieve an overall better performance from the car. We explore how machine learning tools helps in deriving the meaning from the data and analyze them to interpret the performance of the car in conditions which are unmeasurable during the normal lap run. The presentation will conclude with a discussion on further possibilities of use of Machine Learning tools to help an automotive design & validation engineer.