This study explores the design of single-atom alloy (SA) catalysts for methane (CH4) cracking to produce hydrogen (H2). Traditional catalysis methods often suffer from rapid deactivation due to carbon deposition. The researchers use ball milling and machine learning (ML) to identify optimal SA catalysts. They construct a library of 10,950 transition metal SA surfaces and predict C-H dissociation energy barriers using ML. Ir/Ni and Re/Ni are identified as top performers. Notably, Re/Ni achieves a hydrogen yield of 10.7 gH2 gcat−1 h−1 with 99.9% selectivity and 7.75% CH4 conversion at 450 °C, 1 atm. Ball milling enhances catalyst performance by removing deposited carbon, extending its operational lifespan to over 240 hours. The study demonstrates that ML can accelerate the discovery of efficient catalysts, and the byproduct carbon can be used in lithium batteries, showcasing the atomic economy of this approach.This study explores the design of single-atom alloy (SA) catalysts for methane (CH4) cracking to produce hydrogen (H2). Traditional catalysis methods often suffer from rapid deactivation due to carbon deposition. The researchers use ball milling and machine learning (ML) to identify optimal SA catalysts. They construct a library of 10,950 transition metal SA surfaces and predict C-H dissociation energy barriers using ML. Ir/Ni and Re/Ni are identified as top performers. Notably, Re/Ni achieves a hydrogen yield of 10.7 gH2 gcat−1 h−1 with 99.9% selectivity and 7.75% CH4 conversion at 450 °C, 1 atm. Ball milling enhances catalyst performance by removing deposited carbon, extending its operational lifespan to over 240 hours. The study demonstrates that ML can accelerate the discovery of efficient catalysts, and the byproduct carbon can be used in lithium batteries, showcasing the atomic economy of this approach.