Battery technology has been a hot spot for many researchers lately. Electrochemical researchers have been focusing on the synthesis and design of battery materials; researchers in the field of electronics have been studying the simulation and design of battery management system (BMS), whereas mechanical engineers have been dealing with structural safety and thermal management strategies for batteries. However, overcoming battery limitation in only one or two domains will not design an efficient battery pack as it requires an integrated framework. So far, there are few research studies that circumscribed all the multidisciplinary aspects (cell material selection, cell-electrode design, cell clustering, state of health (SOH) estimation, thermal management, cell monitoring, and recycling) simultaneously for battery packs in electric vehicles (EVs). This article presents a holistic engineering design and simulation strategy for a future advanced battery pack and its parts by assimilating paradigmatic solutions for cell material selection, component design, cell clustering, thermal management, battery monitoring, and recycling aspects of the battery and its components. The developed framework has been proposed based on density functional theory (DFT)-based cell material selection, topology design-based cell-electrode design, machine learning (ML)-based SOH estimation along with multidisciplinary design optimization-based liquid cooling system. The proposed framework also highlights the optimal configuration of cells using ML algorithms and multi-objective optimization of cell-assembly parameters. The role of digital twins for real-time and faster acquisition of data has been highlighted for the advanced and futuristic battery pack designs. Furthermore, a preliminary investigation of robot-assisted disassembly and recycling of battery packs has been summarized. Each proposed methodology has been discussed in detail along with advantages and limitations. Critical research orientations are also discussed in the end.