Background <p>Endometriosis, despite its high prevalence, is underdiagnosed and poorly managed due to lack of clinically validated biomarkers and pathophysiological insight. Menstrual blood-derived stem cells have been implicated in disease pathogenesis, but their diagnostic potential remains unexplored.</p> Methods <p>We conducted a case-control clinical study in women (n = 42; 19 endometriosis, 23 controls). Menstrual blood samples were collected, and menstrual blood-derived stem cells were isolated for whole-genome DNA methylation sequencing. Differential methylation analysis was performed to identify disease-specific epigenetic biomarkers, and machine learning models were applied to evaluate the diagnostic performance of candidate markers. An external endometrial single-cell RNA sequencing atlas including endometriosis samples was employed to correlate RNA expression with the identified disease-specific methylation signature.</p> Results <p>Here we identify differentially methylated regions enriched in genes linked to hallmarks of endometriosis such as inflammation, tissue remodelling and development. These differentially methylated regions robustly distinguish cases from controls, independent of technical and clinical variables. Machine learning models trained and validated on these differentially methylated regions achieve high diagnostic performance (specificity 83%, sensitivity 79%). Integration with an independent single-cell RNA sequencing dataset shows that the differentially methylated regions may modulate gene expression, further supporting their biological relevance.</p> Conclusions <p>These findings position menstrual blood-derived stem cell DNA methylation profiling as a promising, non-invasive approach for early endometriosis diagnosis and personalised care.</p>