BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a leading cause of chronic liver disease globally. Current diagnostic methods, such as liver biopsies, are invasive and have limitations, highlighting the need for non-invasive alternatives. AIM: To investigate extracellular vesicles (EVs) as potential biomarkers for diagnosing and staging steatosis in patients with MASLD using machine learning (ML) and explainable artificial intelligence (XAI). METHODS: In this single-center observational study, 798 patients with metabolic dysfunction were enrolled. Of these, 194 met the eligibility criteria, and 76 successfully completed all study procedures. Transient elastography was used for steatosis and fibrosis staging, and circulating plasma EV characteristics were analyzed through nanoparticle tracking. Twenty ML models were developed: Six to differentiate non-steatosis (S0) from steatosis (S1-S3); and fourteen to identify severe steatosis (S3). Models utilized EV features (size and concentration), clinical (advanced fibrosis and presence of type 2 diabetes mellitus), and anthropomorphic (sex, age, height, weight, body mass index) data. Their performance was assessed using receiver operating characteristic (ROC)-area under the curve (AUC), specificity, and sensitivity, while correlation and XAI analysis were also conducted. RESULTS: The CatBoost C1a model achieved an ROC-AUC of 0.71/0.86 (train/test) on average across ten random five-fold cross-validations, using EV features alone to distinguish S0 from S1-S3. The CatBoost C2h-21 model achieved an ROC-AUC of 0.81/1.00 (train/test) on average across ten random three-fold cross-validations, using engineered features including EVs, clinical features like diabetes and advanced fibrosis, and anthropomorphic data like body mass index and weight for identifying severe steatosis (S3). Key predictors included EV mean size and concentration. Correlation, XAI, and SHapley Additive exPlanations analysis revealed non-linear feature relationships with steatosis stages. CONCLUSION: The EV-based ML models demonstrated that the mean size and concentration of circulating plasma EVs constituted key predictors for distinguishing the absence of significant steatosis (S0) in patients with metabolic dysfunction, while the combination of EV, clinical, and anthropomorphic features improved the diagnostic accuracy for the identification of severe steatosis. The algorithmic approach using ML and XAI captured non-linear patterns between disease features and provided interpretable MASLD staging insights. However, further large multicenter studies, comparisons, and validation with histopathology and advanced imaging methods are needed.