<p>Extracellular vesicles (EVs) are nanoscale, membrane-bound particles that carry nucleic acids, proteins, metabolites, and lipids. Their omics profiles can reflect tumor and microenvironmental states, making EVs a promising source of liquid biopsy biomarkers. Machine learning (ML) is well suited to EV omics because it can learn predictive signatures from high-dimensional, correlated, and sparse features and integrate complementary modalities. However, clinical translation is often hindered by EV-specific issues—including heterogeneous vesicle populations, isolation-dependent recovery of subtypes, and co-isolated particles. Few reviews comprehensively synthesize ML studies across EV transcriptomics, proteomics, metabolomics, and lipidomics in oncology. This review introduces EV fundamentals and provides a structured, in-depth evaluation of recent advances in ML-enhanced EV omics for early cancer detection, molecular subtyping, prognosis, and treatment response prediction. Key challenges—including data quality, model generalizability, algorithmic interpretability, ethical considerations, and standardization issues—are critically examined and distilled into practical recommendations for study design, validation, and reporting. Emerging directions include single-vesicle omics, higher-resolution EV profiling, interpretable multimodal fusion, and end-to-end pipelines that integrate EV multi-omics with ML. Together, these advances could help translate EV-based liquid biopsy into clinically useful tools for precision oncology.</p> Graphical Abstract <p></p>