AbstractInorganic lead‐free halide perovskites, devoid of toxic or rare elements, have garnered considerable attention as photocatalysts for pollution control, CO₂ reduction and hydrogen production. In the extensive perovskite design space, factors like substitution or doping level profoundly impact their performance. To address this complexity, a synergistic combination of machine learning models and theoretical calculations were used to efficiently screen substitution elements that enhanced the photoactivity of substituted Cs₂AgBiBr₆ perovskites. Machine learning models determined the importance of d¹⁰ orbitals, highlighting how substituent electron configuration affects electronic structure of Cs₂AgBiBr₆. Conspicuously, d¹⁰‐configured Zn²⁺ boosted the photoactivity of Cs₂AgBiBr₆. Experimental verification validated these model results, revealing a 13‐fold increase in photocatalytic toluene conversion compared to the unsubstituted counterpart. This enhancement resulted from the small charge carrier effective mass, as well as the creation of shallow trap states, shifting the conduction band minimum, introducing electron‐deficient Br, and altering the distance between the B‐site cations d band centre and the halide anions p band centre, a parameter tuneable through d¹⁰ configuration substituents. This study exemplifies the application of computational modelling in photocatalyst design and elucidating structure–property relationships. It underscores the potential of synergistic integration of calculations, modelling, and experimental analysis across various applications.