Recently, a series of new photocatalysts have been developed for to combat diverse bio-recalcitrant contaminants and the inactivation of bacteria. Modeling photocatalytic processes is important to assess these materials, and to understand and optimize their performance. In this study, the recent literature is critically reviewed and analyzed to identify and compare methods of modeling photocatalytic performance. The Langmuir–Hinshelwood model (L-H) has been used in many studies to rationalize the degradation kinetics of single contaminants because it is the simplest model including both the adsorption equilibrium and degradation rates. Other studies report the development of more sophisticated variants of the L-H model that include the rates of catalyst excitation, recombination of electron-hole pairs, production of reactive oxygen species (ROS), and formation of by-products. Modified Chick-Watson (C[sbnd]W) and Hom models have been used by many researchers to include lag phases of bacteria in the description of disinfection kinetics. Artificial neural networks (ANNs) have been used to analyze the effects of operational conditions on photocatalyst performance. Moreover, response surface methodology (RSM) has been employed for experimental design, and optimization of operational conditions. We have reviewed and analyzed all available articles that model photocatalytic activity towards water pollution, summarized and put them in context, and recommended future research directions.