<p>Chemical mechanical polishing (CMP) stands as a critical process in the semiconductor manufacturing industry, essential for achieving the stringent planarity and surface finish required for advanced integrated circuits and semiconductor devices. Despite significant advancements in the CMP process, the ongoing development of stable and efficient polishing methods, along with their successful integration into semiconductor fabrication, remains challenging. These challenges arise from the diverse range of materials and device architectures, necessitating meticulous consideration and optimization. Traditional research strategies are often characterized by their sluggishness and labor-intensive nature. In the epoch driven by data, the progression of machine learning (ML) in the domains of materials and device design has become an irreversible trend. The proficiency and effectiveness of ML in addressing nonlinear and complex game-theoretic issues often surpass those of conventional simulation software and trial-and-error methodologies. However, the application of ML in the CMP field has only started to flourish in recent years, necessitating a comprehensive review of the relevant ML technologies, their current application status, the urgent challenges faced, and the future development blueprints. The review concludes with insights on the aforementioned topics, and it is hoped that this discussion can offer valuable guidance for the design and development of highly efficient and reliable CMP processes.</p>