Traditionally, the development and optimisation of photocatalytic materials have relied on experimental approaches and density functional theory (DFT) calculations. Although these methods have driven significant scientific progress, they are increasingly constrained by high computational costs, lengthy development cycles, and limited scalability. In recent years, machine learning (ML) has emerged as a powerful and sustainable alternative, offering a data-driven framework that accelerates materials discovery through rapid and accurate property prediction. This review highlights the essential components of the ML workflow data collection, feature engineering, model selection, and validation while exploring its application in predicting photocatalytic properties. It further discusses recent advances in forecasting key characteristics such as band edge positions, charge carrier mobility, and surface reactivity using both supervised and unsupervised ML techniques. Persistent challenges, including data scarcity, model interpretability, and generalisability, are also addressed, alongside potential strategies to improve the robustness and reliability of ML-driven materials design. By combining high prediction accuracy with superior computational efficiency, ML holds the potential to revolutionise high-throughput screening and guide the systematic development of next-generation photocatalysts.