• Comprehensive review of ML/AI in PQD electroanalysis. • Data-driven algorithms accelerate PQD synthesis and sensor design. • Links descriptors to electrochemical parameters (Rct, Cdl, efficiency). • Discusses limits: data heterogeneity, model interpretability, stability. • Trends in hybrid quantum-classical learning and autonomous PQD platforms. The integration of machine learning (ML) and artificial intelligence (AI) into perovskite quantum dot (PQD) research has revolutionized electroanalytical science by enabling predictive control over synthesis, interfacial design, and signal interpretation. This review—the first comprehensive survey in this emerging field—systematically explores how data-driven algorithms accelerate PQD discovery, optimize electronic and ionic transport at electrode interfaces, and enhance analytical performance in electrochemical sensing, catalysis, and point-of-care diagnostics. ML paradigms such as Bayesian optimization, reinforcement learning, and graph neural networks are analyzed in the context of autonomous PQD synthesis, defect engineering, and electroanalytical signal deconvolution. The review further outlines mechanistic correlations between algorithmic parameters and electrochemical metrics, including charge transfer resistance, double-layer capacitance, and faradaic efficiency. Challenges such as data heterogeneity, model interpretability, and operational stability are discussed alongside emerging directions in quantum–classical hybrid learning and federated electroanalytical networks. This work provides a unified framework that bridges AI-driven materials informatics with practical electroanalysis, paving the way for intelligent, self-optimizing electrode systems.
Shuheil et al. (2026) studied this question.