Cities are getting smarter every day. Municipalities are increasingly using information and communication technologies (ICT) to enrich and enhance city life, which is paramount in planning the cities of the future. This calls for connected and automated cars in the future. This emphasizes the need for efficient and fast acting controls algorithms in Engine Management System (EMS) to achieve enhanced dynamic response of the connected mobility. Artificial Intelligence and Machine learning in development of control algorithms is of paramount need. Advancements in internal combustion engine technologies have increased a need of complex features. Effective control of these features is the need for the day. The optimum solution of these features via parameter optimization needs to be effectively derived for entire engine operating range in steady and transient operation. This is very critical in case of urban mobility where synchronized and harmonized management is the need of the hour. Today developers apply supervised learning to powertrain calibration, missing physics and signature, emission control and virtual sensor development.This paper presents the implementation of machine learning algorithms for predictive models via optimal set of parameters for various powertrain features for synchronized mobility. Predictive models amalgamated with Artificial Intelligence will accurately determine the response of a system. Machine learning techniques is used to create a predictive model when no knowledge of the system is known and difficult to determine. The paper also presents the rethinking of conventional process by introducing Machine learning in all the phases mentioned: Concept Development SOP series volume production. © 2019 IEEE.