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During the design conception of an automobile, typically low-fidelity physics-based simulations are coupled with engineering judgement to define key architectural components and subsystems which limits the capability to identify NVH issues arising from systems interaction. This translates to non-optimal designs because of unexplored design opportunities and therefore, lost business efficiencies. The sparse design information available during the design conception phase limits the development of representative higher fidelity physics-based simulations. To address that restriction on design optimization opportunities, this paper introduces an alternate approach to develop reduced order predictive models using regression techniques by harnessing historical measurement and simulation data. The concept is illustrated using two driveline NVH phenomenon: axle whine and take-off shudder.