This paper introduces a novel hybrid approach, termed STO-IWGAN, aimed at enhancing energy optimization in fuel cell electric vehicles (FCEVs). The key to maintaining the system's normal operation is the energy management plan and hybrid system performance optimization. The STO-IWGAN method combines the Siberian Tiger Optimization (STO) and an enhanced Wasserstein Generative Adversarial Network (IWGAN). The primary objectives of this approach include improving energy efficiency, reducing fuel usage, and enhancing overall FCEV performance. The STO method is utilized to optimize the operating parameters of the fuel cell arrangement, while the IWGAN method predicts the vehicle's power demand. The proposed technique is implemented and evaluated using MATLAB, benchmarked against contemporary methods. Comparative analysis reveals that the STO-IWGAN method outperforms existing approaches such as heap-based optimization, particle swarm optimization, and wild horse optimization. This work aims to contribute to research on increasing the hybrid power system's energy utilization efficiency and fuel cell the lifespan, offer recommendations for optimal control strategy and structural design, and offer additional ideas for future energy management optimization. The efficiency value of the proposed method, recorded at 95 %, surpasses that of other existing techniques, indicating its superior performance in energy optimization for FCEVs. The Existing HBO method efficiency value is 85 %, PSO method efficiency value is 75 % and the WHO method efficiency value is 65 %. The proposed method efficiency value is higher than other existing method.