This paper presented a novel multistage energy optimization framework integrating the Volterra filtered-xLMS algorithm with artificial intelligence (AI) techniques to enhance the performance and efficiency of complex systems. The proposed approach uses the nonlinear and adaptive modeling capabilities of the Volterra series to achieve optimization of energy usage dynamically across several stages, while being capable of adapting to any variation in the operating conditions in real time. AI-based controllers are employed to predict and regulate the dynamics of the system, thereby promoting efficiency and stability. The hybrid approach is particularly suited for systems where accurate energy management is extremely important, for example, electric vehicles, renewable energies, and industrial automation. The effectiveness of the proposed technique is validated through numerical simulations and analytical studies, with dramatic improvement in energy optimization, reduced power losses, and improved system reliability. The findings highlighted the potential of fusion of new control algorithms with AI in alleviating energy problems in modern embedded engineering systems.