To control the photocatalytic activity, it is essential to consider several parameters affecting the structure of ordered multicomponent TiO2-based photocatalytic nanotubes. The lack of systematic knowledge about the relationship between structure, property, and preparation parameters may be provided by applying a machine learning (ML) methodology and predictive models based on the quantitative structure-property-condition relationship (QSPCR). In the present study, for the first time, the quantitative mapping of preparation parameters, morphology, and photocatalytic activity of 136 TiO2 NTs doped with metal and non-metal nanoparticles synthesized with the one-step anodization method has been investigated via linear and nonlinear ML methods. Moreover, the developed QSPCR model, for the first time, provides systematic knowledge supporting the design of effective TiO2-based nanotubes by proper structure manipulation. The proposed computer-aided methodology reduces cost and speeds up the process (optimize) of efficient photocatalysts’ design at the earliest possible stage (before synthesis) in line with the sustainability-by-design strategy.