The great promise of circulating extracellular vesicles (cEVs) revolutionising biological and biomedical sciences in humans hinges heavily in our ability to resolve and dissect their molecular composition (such as proteins and lipids) to sufficient granularity against the backdrop of exceptionally complexity of human plasma. Although mass spectrometry (MS) has emerged as a powerful identification and quantification tool, non-EV particles such as lipoprotein particles and soluble proteins (>99% of plasma proteins by weight) riddle our EV-isolation pipelines and challenge the dynamic range of mass spectrometry (MS), resulting in incomplete, low-coverage, potentially incorrect, EV -omics data that negatively influence human studies that are often sample-limited. 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 and biochemical characterisation verify their EV identify and support high degree of separation from non-EV particles. Using multiple machine learning approaches, we constructed two independent models comprising a cohort of 200 proteins and 152 lipid species that can distinguish EVs from non-EV particles in plasma with 100% specificity and sensitivity, highlighting their use as a biological marker for cEVs in human population. Moreover, several of these features display 100% detection rate in cEVs from multiple plasma sources using MS (operated in DIA or DDA mode, labelled vs label-free) and display concomitant enrichment in EVs compared to their parental cells. We propose these conserved proteins and lipids features as hallmark molecular features of circulating EVs. This knowledge will be instrumental in quality assessments of MS-based high through-put screening of cEVs in human studies, facilitate knowledge transferability of EV biology into humans, streamline development of more precise EV isolation pipeline and analytics, and extend international guidelines on standardization and scientific rigor to circulation EV research.