Abstract Extracellular vesicles (EVs) are emerging as naturally bioactive nanomaterials with intrinsic biocompatibility and targeting potential. Recent integration of machine learning (ML) into EV research has accelerated advances in molecular profiling, structure–function prediction, and rational design of vesicle-based therapeutics. Yet, the inherent complexity and heterogeneity of EV populations pose major analytical challenges. Concurrently, machine learning is revolutionizing biomedical science by uncovering patterns in high dimensional, multimodal datasets. In EV research, ML has enabled major advances across automated imaging, multi omics integration, disease classification, therapeutic engineering, and standardization. This review presents a comprehensive synthesis of ML-enabled EV studies, organized by data modality (imaging, omics, cytometry), algorithmic paradigm (CNNs, random forests, autoencoders, GNNs), and translational application (diagnosis, prognosis, drug delivery, manufacturing QC). Unlike prior reviews that have typically considered EV biology and AI methods in relative isolation, we introduce a unified three-axis taxonomy that explicitly links EV data modalities, machine learning architectures, and clinical use-cases, thereby providing a structured map of the field. We discuss key technical barriers including data sparsity, batch variability, and model explainability and spotlight frontier developments such as federated learning, self-supervised models, and real-time EV analytics. At the nexus of computational intelligence and nanomedicine, ML-enhanced EV platforms are rapidly progressing from fragmented innovations to clinically actionable systems. This review offers a roadmap for advancing AI-integrated EV technologies in cancer precision medicine. Graphical abstract
Tiwari et al. (Sat,) studied this question.