The Oxygen Evolution Reaction (OER) is a fundamental process in electrochemical water splitting, playing
a crucial role in sustainable hydrogen production. However, its intrinsically sluggish kinetics, involving
complex four-electron transfer steps, remain a major bottleneck for efficient energy conversion. In recent
years, Machine Learning (ML) has emerged as a powerful approach to accelerate catalyst discovery by
enabling data-driven prediction of OER activity and reducing reliance on costly experimental and density
functional theory (DFT) calculations. This review systematically summarizes recent advances in ML�assisted OER research, focusing on key aspects including dataset construction, descriptor engineering,
model development, and performance evaluation. Various ML techniques, ranging from traditional
algorithms such as Random Forest and Support Vector Machines to advanced deep learning approaches,
are critically discussed in the context of catalyst screening and activity prediction. Particular attention is
given to the role of physicochemical descriptors, including adsorption energies and electronic structure
parameters, in governing model performance and interpretability. Furthermore, this review highlights
current challenges, such as data scarcity, lack of standardization, and limited model generalization, and
discusses emerging trends, including active learning, explainable AI, and integration with high-throughput
simulations. By providing a comprehensive overview, this work aims to guide future research toward the
development of robust, interpretable, and scalable ML frameworks for accelerating the discovery of
efficient OER catalysts.