AbstractCopper antimony sulfides are regarded as promising catalysts for photo‐electrochemical water splitting because of their earth abundance and broad light absorption. The unique photoactivity of copper antimony sulfides is dependent on their various crystalline structures and atomic compositions. Here, a closed‐loop workflow is built, which explores Cu–Sb–S compositional space to optimize its photo‐electrocatalytic hydrogen evolution from water, by integrating a high‐throughput robotic platform, characterization techniques, and machine learning (ML) optimization workflow. The multi‐objective optimization model discovers optimum experimental conditions after only nine cycles of integrated experiments–machine learning loop. Photocurrent testing at 0 V versus reversible hydrogen electrode (RHE) confirms the expected correlation between the materials’ properties and photocurrent. An optimum photocurrent of −186 µA cm⁻² is observed on Cu–Sb–S in the ratio of 9:45:46 in the form of single‐layer coating on F‐doped SnO₂ (FTO) glass with a corresponding bandgap of 1.85 eV and 63.2% Cu¹⁺/Cu species content. The targeted intelligent search reveals a nonobvious CuSbS composition that exhibits 2.3 times greater activity than baseline results from random sampling.