The development of highly active metal-based single-atom catalysts (SACs) is crucial for energy conversion and storage, offering optimized atom utilization and high catalytic activity, with bifunctional SACs for hydrogen evolution (HER) and oxygen evolution/reduction (OER/ORR) reactions providing greater efficiency and cost-effectiveness than monofunctional catalysts, making them scientifically and economically valuable. By integrating density functional theory and machine learning methods, we systematically evaluated the potential of TM-N