The article delves into the optimization of methane decomposition to produce hydrogen efficiently, addressing challenges faced by traditional methods such as dry reforming. By utilizing single-atom alloy (SAA) catalysts, the research aims to simplify methane decomposition and enhance selectivity by leveraging the uniform active sites of SAAs. The study emphasizes the importance of bridging gaps in kinetic parameters, particularly in methane dry reforming, through the integration of machine learning and density functional theory techniques. By training ML models with DFT results and utilizing advanced descriptors, the research predicts and validates energy barriers for C-H dissociation on various SAA surfaces, ultimately aiming to accelerate the development and optimization of catalysts for hydrogen production. Notably, SAAs containing specific host metals like Fe, Co, and Ni exhibit promising catalytic performance for methane decomposition. The study underlines the significance of accurate predictive models in improving catalyst screening efficiency and reducing the resources required for experimental testing, thus contributing to advancements in sustainable energy solutions.