Machine Learning-Guided Carbon Dots for Redox Nanotheranostics: From Predictive Design to Intelligent Drug Delivery

The convergence of machine learning (ML) and carbon dots (CDs) nanotechnology is expanding opportunities for predictive design of redox-responsive theranostic systems beyond conventional empirical optimization. This review examines recent advances in ML-guided CDs for redox nanotheranostics, with particular emphasis on intelligent drug delivery, diagnostic sensing, and integrated therapeutic platforms. The article first establishes the biological and physicochemical foundations of redox regulation, highlighting the relationships among oxidative signaling, CD surface chemistry, and therapeutic functionality. It then analyzes how ML transforms multidimensional experimental datasets into predictive frameworks capable of identifying structure–property–performance relationships, prioritizing critical design variables, and accelerating nanomaterial optimization. Particular attention is devoted to learning architectures, feature engineering, mechanistic interpretability, and the emerging concept of closed-loop design. Importantly, current evidence is distinguished from fully autonomous optimization: most reported studies remain limited to single-cycle prediction–validation workflows, and high within-dataset predictive performance has rarely been tested through independent cross-laboratory or cross-biological validation. The review further synthesizes evidence on ML-assisted CD systems for optical engineering, redox modulation, controlled drug delivery, molecular sensing, and diagnostic intelligence. Major challenges include mechanistic uncertainty, dataset heterogeneity, limited external validation, reproducibility, scalability, and clinical translation. Future progress will require mechanistically informed AI, standardized data infrastructures, prospective validation, and genuinely iterative design frameworks capable of converting predictive performance into transferable and clinically meaningful nanotheranostic strategies.

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