A machine learning (ML) approach is used to design single-atom alloy (SAAs) catalysts for methane cracking. The study identifies Re/Ni and Ir/Ni as top-performing SAAs, achieving high hydrogen yields and selectivity at 450°C. ML models predict C-H dissociation energy barriers for 10,950 SAA surfaces, enabling efficient screening of catalysts. The Re/Ni catalyst demonstrates a hydrogen yield of 10.7 gH₂/gcat/h with 99.9% selectivity and 7.75% methane conversion. Ball milling is employed to remove carbon deposits, enhancing catalyst longevity. The study also explores the application of solid carbon by-products as electrode materials in lithium batteries, showing superior performance compared to commercial carbon black. The work highlights the potential of ML in catalyst design and the effectiveness of SAAs in methane cracking, with Re/Ni achieving a 240-hour operational lifetime. The results demonstrate the importance of ML in accelerating catalyst development and improving catalytic efficiency.A machine learning (ML) approach is used to design single-atom alloy (SAAs) catalysts for methane cracking. The study identifies Re/Ni and Ir/Ni as top-performing SAAs, achieving high hydrogen yields and selectivity at 450°C. ML models predict C-H dissociation energy barriers for 10,950 SAA surfaces, enabling efficient screening of catalysts. The Re/Ni catalyst demonstrates a hydrogen yield of 10.7 gH₂/gcat/h with 99.9% selectivity and 7.75% methane conversion. Ball milling is employed to remove carbon deposits, enhancing catalyst longevity. The study also explores the application of solid carbon by-products as electrode materials in lithium batteries, showing superior performance compared to commercial carbon black. The work highlights the potential of ML in catalyst design and the effectiveness of SAAs in methane cracking, with Re/Ni achieving a 240-hour operational lifetime. The results demonstrate the importance of ML in accelerating catalyst development and improving catalytic efficiency.