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Abstract INTRODUCTION Alzheimer's disease (AD), the most prevalent neurodegenerative disorder globally, has emerged as a significant health concern. Recently it has been revealed that extracellular vesicles (EVs) play a critical role in AD pathogenesis and progression. Their stability and presence in various biofluids, such as blood, offer a minimally invasive window for monitoring AD‐related changes. METHODS We analyzed plasma EV‐derived messenger RNA (mRNA) from 82 human subjects, including individuals with AD, mild cognitive impairment (MCI), and healthy controls. With next‐generation sequencing, we profiled differentially expressed genes (DEGs), identifying those associated with AD. RESULTS Based on DEGs identified in both the MCI and AD groups, a diagnostic model was established based on machine learning, demonstrating an average diagnostic accuracy of over 98% and showed a strong correlation with different AD stages. DISCUSSION mRNA derived from plasma EVs shows significant promise as a non‐invasive biomarker for the early detection and continuous monitoring of AD. Highlights The study conducted next‐generation sequencing (NGS) of mRNA derived from human plasma extracellular vesicles (EVs) to assess Alzheimer's disease (AD). Profiling of plasma EV‐derived mRNA shows a significantly enriched AD pathway, indicating its potential for AD‐related studies. The AD‐prediction model achieved a receiver‐operating characteristic area under the curve (ROC‐AUC) of more than 0.98, with strong correlation to the established Clinical Dementia Rating (CDR).