The great promise of circulating extracellular vesicles (cEVs) revolutionizing biological and biomedical sciences in humans hinges heavily in our ability to resolve their molecular composition (e.g., proteins and lipids) to sufficient granularity against the backdrop of exceptional complexity of human plasma. Although mass spectrometry (MS) emerges as a powerful identification and quantification tool, non-EV particles including lipoprotein particles and soluble proteins (>99% of plasma proteins by weight) riddle our EV-isolation pipelines and challenge dynamic range of MS, resulting in incomplete, low-coverage, potentially incorrect EV-omics data. Here, using MS we construct a high-confident proteome (>5000 proteins) and lipidome (900 lipid species, over 40 classes) blueprint of cEVs (>100 samples) isolated by high-resolution density gradient separation of human plasma (0.5 ml). Extensive biophysical/biochemical characterization verify their EV identify and support high degree of separation from non-EV particles. Using multiple machine learning approaches, we construct two independent models comprising a cohort of 200 proteins and 152 lipid species that can distinguish EVs from non-EV particles with 100% accuracy, highlighting their use as biological markers in humans. Moreover, several of these features display 100% detection rate in cEVs from multiple plasma sources using MS (operated in DIA/DDA mode, labelled vs label-free), and conservation and enrichment in EVs from cell cultures. We propose these conserved features as hallmark molecular features of cEVs, which will facilitate quality assessments of MS-based high through-put screens, knowledge transferability of EV biology into humans, development of precise EV isolation pipelines/analytics, and standardization and scientific rigor to human EV research.