Carbon quantum dotsQuantum dots (CDs)Carbon quantum dots (CDs) have attracted considerable attention because of their unique characteristics, setting them apart from other nanomaterialsNanomaterials and intriguing researchers from diverse fields like chemistry, physics, biology, and materials science. The significant consideration of predictive modelingPredictive modeling applications for CDs has been evident in recent years, particularly through integrating machine learning (MLMachine learning (ML)) algorithms. These applications have played a crucial role in optimizing, synthesizing, evaluating, and utilizing CDs in various research and development endeavors. Various supervised learning regression and classification models like artificial neural networksArtificial neural networks (ANN), decision treesDecision tree, random forestsRandom forest (RF), k-Nearest neighborsK-Nearest Neighbor, and support vector machinesSupport vector machine (SVM) have been utilized to analyze and forecast key propertiesKey Properties of CDs. The MLMachine learning (ML) approach not only streamlines the process of CDs development but also enhances comprehension of the intricate connections between precursor composition, synthesis methods, and CDs properties. This chapter presents and discusses a variety of MLMachine learning (ML) algorithms and statistical techniques to comprehensively explore the use of different MLMachine learning (ML) methods in predicting the properties and attributes of CDs, enhancing the synthesis and modification of CDs, and optimizing the application of CDs across various sectors. The discussion covers the obstacles and possible results of implementing MLMachine learning (ML) techniques and predictive modelingPredictive modeling in research on CDs.