Due to their diverse physicochemical properties and varied beneficial characteristics such as high stability, superior biocompatibility, unique optical properties, economical and eco-friendly synthesis pathway, presence of numerous functional groups, and excellent electron mobility, carbon dots (CDs) have been a subject of great interest among researchers. Artificial intelligence (AI), including machine learning (ML), has emerged as a transformative force in the realm of materials science, offering a diverse array of models to decipher complex relationships between material properties and structures. Using the experimental data of the previously reported research, algorithms are designed to create the link between the input and the output parameters. Once the link is established, a little mutation with experimentally designed parameters may readily provide the required features of the nanomaterial. Among the many attributes, quantum yield (QY) is regarded as one of the most significant features from an application perspective. Currently, AI-based CDs are being synthesized to get the appropriate quantum yield and are subsequently used in many sectors.