The automotive and off-road industries are heavily investing in research and development (R&D) to improve both physical and virtual verification and validation techniques. Recent software and hardware advancements have extended these techniques from simple component evaluations to complex system assessments such as involving multi-physics scenarios. Despite the benefits of virtual validation tools like structural analysis and computational fluid dynamics (CFD), they often come with high development costs, particularly in CFD applications. Virtual verification methodology, especially when combined with data science, offer significant advantages over traditional physical methods by enhancing computer-aided engineering (CAE) efficiency and reducing resource consumption which can greatly improve product design and validation efficiency across many industries. The success of machine learning applications depends on effective data processing, adequate computational resources, and the right algorithm selection. Key machine learning techniques impacting the CFD field include data analytics, parametric-based machine learning (PBML), reduced-order models, geometry deep learning, and physics-informed neural networks. This study demonstrates the potential of machine learning in the CFD field by utilizing numerical data-driven techniques such as data analytics and PBML on various components related to flow and thermal systems, including intake system, cooling system components, aftertreatment system and exhaust-driven aspiration. Selecting the right regression model is crucial for achieving optimal design solutions. The study concludes by highlighting how different regression techniques can influence the quality of design decisions. These methods can be scaled for applications where results heavily depend on parametric input variables, enabling the prediction of quantitative outcomes. The efficient PBML approach can alleviate the computational demands and time-consuming aspects associated with traditional methods.