Blending TiO2 and cement to create photocatalytic composites holds promise for low-cost, durable water treatment. However, the efficiency of such composites hinges on cross-effects of several parameters such as cement composition, type of photocatalyst, and microstructure, which are poorly understood and require extensive combinatorial tests to discern. Here, we report a new combinatorial data science approach to understand the influence of various photocatalytic cement composites based on limited datasets. Using P25 nanoparticles and submicron-sized anatase as representative TiO2 photocatalysts and methyl orange and 1,4-dioxane as target organic pollutants, we demonstrate that the cement composition is a more influential factor on photocatalytic activity than the cement microstructure and TiO2 type and particle size. Among the various cement constituents, belite and ferrite had strong inverse correlation with photocatalytic activity, while natural rutile had a positive correlation, which suggests optimization opportunities by manipulating the cement composition. These results were discerned by screening 7806 combinatorial functions that capture cross-effects of multiple compositional phases and obtaining correlation scores. We also report •OH radical generation, cement aging effects, TiO2 leaching, and strategies to regenerate photocatalytic surfaces for reuse. This work provides several nonintuitive correlations and insights on the effect of cement composition and structure on performance, thus advancing our knowledge on development of scalable photocatalytic materials for drinking water treatment in rural and resource-limited areas.