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Pancreatic cancer (PC) is a major global public health problem. Identifying potential biomarkers and individual patient characteristics that contribute to early PC detection is critical in clinical practice. The present study employed a comprehensive data-driven pipeline to address this important issue in a subset of patients diagnosed with Pancreatic Ductal Adenocarcinoma (PDAC) along non-oncologic samples. Putative extracellular vesicle (EV) characteristics in combination with clinical and laboratory features served as potential predictors of patient risk stratification and PDAC diagnosis. The machine learning (ML)-based pipeline entailed the appropriate steps for unbiased model training and validation. The two groups of samples were discriminating against a high degree of accuracy (= 0.96) with balanced sensitivity (=0.95) and specificity (=0.97) rates. Both EV-based variables and biochemical characteristics emerged as important predictors of PDAC diagnosis.Clinical Relevance-Minimally invasive technologies based on extracellular vesicles (EVs) along with adaptive learning methodologies could provide new directions and solutions with increased efficiency in PC clinical diagnosis.