Accurate and reliable nuclear reaction cross section data are crucial for nuclear reactor design, nuclear energy development, and nuclear safety assessments. Traditional methods for generating nuclear data face significant challenges, including high costs, long time frames, and incomplete coverage. Recent advances in machine learning (ML) offer new opportunities for nuclear data generation, but existing methods struggle with the scarcity of experimental data, limiting their ability to generate high-precision and broadly applicable cross section data. This paper introduces the Hierarchical Ensemble Generation Model (HEGM), a novel AI-driven approach to nuclear reaction cross section generation. HEGM combines transfer learning, meta-learning, prototype networks, and generative adversarial networks to address the challenges of sparse data and improve predictive accuracy. We evaluate HEGM's performance on isotopes