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Combustion engine driven vehicles operating in a connected and autonomous vehicle (CAVs) environment, the engine drive cycles are run in a regulated manner. This is due to synchronized movement of vehicles operating in connected environment. Hence, developing intelligent and faster control of airpath variable with smooth transient tracking, helps to achieve a synchronized drive cycle. With regards to this author discuss modeling of turbocharger. This is critical for airpath system variable calculation. Due to the hybridized nature of turbocharger models, predicting accurately the position of VTG without introduction of any sensing devices is key, as sensing device induces delay in action. Authors propose a model which improve the performance and capability of VTG position prediction. A neural network based supervised learning model is developed. This model is coupled with engine models which are in series application for performance evaluation.