Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Abstract Carbon‐based quantum dots (CQDs) have emerged as a versatile class of fluorescent nanomaterials with broad applications in optoelectronics, sensing, and biomedicine; however, their intrinsic structural and chemical complexity poses significant challenges to mechanistic understanding and rational regulation. Machine learning (ML) provides a powerful approach for analyzing complex experimental datasets, uncovering hidden correlations, and enabling insights beyond conventional empirical methodologies. This review summarizes recent ML‐driven advances in CQDs research, with a particular emphasis on fluorescence mechanisms, regulation strategies, and application‐relevant performance optimization, while critically examining fundamental challenges related to interpretability, generalizability, and data reliability. Finally, perspectives on future ML‐assisted frameworks for advancing CQDs toward practical applications are provided.