The search for new materials has been fundamental in developing innovative and sustainable solutions for current industrial challenges. In particular, carbon dots (CDs) represent a promising research field due to their unique characteristics and wide range of applications, including biomedicine, sensors, and catalysis . However, producing CDs with desirable properties remains an intricate and empirical process, requiring extensive experimental effort . In this scenario, computational tools, particularly machine learning (ML) models, have emerged as valuable assets for optimizing synthesis processes and investigating the complex relationship between synthesis parameters and functional performance .