The introduction of newer technologies, like powertrain electrification, active suspension, and driver assist modules (ADAS components), has made vehicle architecture more complex than ever. The ever-increasing need to bring these new technologies faster to market has led to a reduction in the automotive product development cycle. The manufacturers introduce new technologies in stages to facilitate market transition. This leads to hybridization in products and a need for variant management in vehicle platforms, for example carlines can have powertrain variants such as Internal Combustion Engine (ICE), Battery Electric Vehicles (BEV) or Plugin Hybrid Electric Vehicles (PHEV). These result in complex and overlapping subsystem modes unique to each vehicle configuration. Various subsystems in vehicles are designed by decoupling the excitation frequency from the road and powertrain to avoid resonances with structural and acoustic frequencies. Numerous structural modes of subsystems could be body bending or torsion, suspension hop or tramp, powertrain roll or yaw or pitch modes, tire modes, steering column, etc. A modal map is usually used in the automotive industry to separate major vehicle modes using a modal separation approach. Identification of modes for various subsystems using the conventional approach is very challenging. This involves the manual identification of frequencies based on visual examination of mode shapes. This is purely manual and subjective to an engineer’s judgment. This paper attempts to introduce a process that allows for a more streamlined approach to modal map creation by reducing the reliance on subjective evaluation. The paper is based on the reduced model integration approach using FRF-based reduction method. This process will be illustrated using a typical automotive case study model. The reduced model approach and load case definition for this process are established through the BETA CAE NVH Console tool. The modal classification of various subsystems is carried out using BETA CAE Metapost using the system mode classification tool for modal map generation. The paper could be extended to include the Machine Learning (ML) NVH mode classification.