Extracellular Vesicles (EVs) have emerged as important mediators of intercellular communication that package and disseminate biochemical signals. This newly recognized mode of communication between the cells has brought unprecedented therapeutic and diagnostic opportunities making them attractive nanocarriers for clinical and industrial translation. As the EV industry rapidly grows, there is a rising demand for strategies that facilitate EV manufacturing. One of the most vexing issues in the field is a method of EV isolation that can offer reliability, purity, speed, and reproducibility and meet the stringent manufacturing standards of the pharmaceutical industry. To overcome this challenge, in the first part of my thesis, I propose a new highyield and rapid (<20 min) real-time EV isolation method called Size Exclusion – Fast Performance Liquid Chromatography (SE-FPLC). We show that our method can effectively isolate EVs from multiple sources, including EVs derived from human and mouse cells and biofluids. The results indicate that our SE-FPLC platform can successfully remove highly abundant protein contaminants, such as albumin and lipoprotein complexes, which currently represent a significant hurdle in the largescale isolation of EVs for clinical translation. Additionally, the high-yield nature of SE-FPLC allows for easy industrial upscaling of EV production for various clinical utilities. Moreover, SE-FPLC enables analysis of very small volumes of blood for use in point-of-care diagnostics in the clinic. Collectively, our platform offers many advantages over current EV isolation methods and offers rapid clinical utility potential. Once the EVs are isolated, it is imperative to perform EV physicochemical characterization as particle shape and particle charge is pivotal in immune cell interaction. Bulk ensemble methods quantify EV composition but mask heterogeneity. Studying single-vesicle heterogeneity is vital, especially given their emerging role as therapeutic cargos. In the second part of my thesis, I developed a label-free method: to image, perform high-quality biological segmentation using a custom pre-trained neural network model, and quantify and classify single EVs purified from a diverse set of samples. Evaluating the heterogeneity of EVs is crucial for unraveling their complex actions and biodistribution. We identified consistent architectural heterogeneity of EVs using cryogenic transmission electron microscopy (cryo-TEM). Imaging EVs isolated using different methodologies from distinct sources such as cancer cells, normal cells, and body fluids, we identify a structural atlas of their dominantly consistent shapes. We identify EV architectural attributes by utilizing a segmentation neural network model. In total, 7,600 individual EVs were imaged and quantified by our computational pipeline. Across all 7,600 independent EVs, the average eccentricity was 0.5366, and the average equivalent diameter was 132.43 nm. The architectural heterogeneity was consistent across all sources of EVs, independent of purification techniques, and compromised of single spherical (S. Spherical), rod-like or tubular, and double shapes. This openly accessible data and computation toolkit will serve as a reference foundation for high-resolution EV images and offer insights into potential biological impact.