With the increasing global emphasis on carbon dioxide emissions reduction， electrocatalytic carbon dioxide reduction （ECO₂R） to methanol has garnered significant attention within the context of carbon neutrality. However， existing ECO₂R catalysts still suffer from limitations in activity， selectivity， and stability， thereby constraining their practical applications. This underscores the urgent need for the development of highly efficient catalysts， which remains a central research focus in this field. Traditional catalyst design predominantly relies on trial-and-error approaches， which are inherently inefficient. Therefore， novel strategies are required to accelerate catalyst discovery and optimization. With the rapid advancement of artificial intelligence， machine learning has emerged as a powerful tool to drive catalyst development. This review systematically summarizes the reaction mechanisms underlying ECO₂R to methanol and highlights recent advancements in catalyst research， encompassing Cu-based， non-Cu-based， and phthalocyanine-based catalysts. Furthermore， the fundamental framework of machine learning applications in this domain is introduced， covering key stages from data acquisition to model validation. Particular emphasis is placed on machine learning-driven predictions of catalytic activity， catalyst design， and performance optimization. Although machine learning has made remarkable progress in ECO₂R research， there are still several challenges， including data scarcity， insufficient model interpretability， and the lack of a universal prediction framework. Future research should focus on the establishment of high-quality catalyst databases， enhancement of model interpretability， and improvement of generalization capabilities. This review aims to provide a comprehensive perspective on ECO₂R catalyst design while emphasizing the pivotal role of machine learning in facilitating breakthroughs in this field.