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• This review provides a comprehensive overview of GSCD synthesis from renewable, biomass-derived precursors. • This review presents the applications of GSCDs in pharmaceutical sensing. • This review introduces the integration of machine learning (ML) strategies to optimize synthesis parameters and address precursor variability. • This review critically examines current challenges and future directions in GSCD synthesis. Green-synthesized carbon dots GSCDs which are derived from natural, renewable sources such as biomass and bio-derived chemicals, have emerged as a promising class of eco-friendly nanomaterials. Owing to their unique physicochemical properties including strong fluorescence, high water solubility, low toxicity, biocompatibility, and excellent photostability. GSCDs offer significant advantages in biomedical and pharmaceutical applications. Their potential for sensitive and selective drug sensing in dosage forms, biological fluids, and environmental samples has gained increasing attention. Green synthesis approaches, such as hydrothermal treatment, microwave irradiation, ultrasonic methods, and chemical oxidation, provide a sustainable alternative to conventional synthetic routes. This review highlights various natural precursors and green synthetic methods used to produce GSCDs, and discusses their advantages in pharmaceutical drug detection. Additionally, it explores the role of machine learning (ML) in optimizing the synthesis of GSCDs. Furthermore, this review identifies current research gaps and proposes future directions to advance the application of GSCDs in pharmaceutical analysis.
Elagamy et al. (Fri,) studied this question.