The rapid evolution of electrified transportation systems has intensified the need for advanced control strategies that seamlessly integrate vehicle dynamics and energy management. This study presents a comprehensive investigation into the synergistic fusion of intelligent algorithms applied to automotive motion control and energy storage regulation to enhance the efficiency, reliability, and sustainability of next-generation electric mobility. The research explores the convergence of machine learning, adaptive control, and predictive optimization techniques within electrified powertrain architectures. A hybrid methodological framework is developed, combining data-driven modeling with system-level optimization to address real-time challenges in dynamic vehicle behavior and battery performance. The study evaluates how coordinated control between propulsion systems and energy storage units can reduce energy losses, improve range, and enhance operational stability under varying driving conditions. Simulation-based experiments and analytical modeling are employed to assess system performance across multiple scenarios, including urban and highway driving cycles. Results indicate that integrated intelligent control significantly outperforms traditional isolated systems by improving energy efficiency and reducing system stress. The findings contribute to the development of robust, scalable, and intelligent electrified mobility solutions, offering critical insights for future research and industrial applications in sustainable transportation systems.