Purpose: Studies to determine epigenetic changes associated with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) remain scarce; however, current evidence clearly shows that methylation patterns of genomic DNA and noncoding RNA profiles of immune cells differ between patients and healthy subjects, suggesting an active role of these epigenetic mechanisms in the disease. The present study compares and contrasts the available ME/CFS epigenetic data in an effort to evidence overlapping pathways capable of explaining at least some of the dysfunctional immune parameters linked to this disease. Methods: A systematic search of the literature evaluating the ME/CFS epigenome landscape was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria. Differential DNA methylation and noncoding RNA differential expression patterns associated with ME/CFS were used to screen for the presence of transposable elements using the Dfam browser, a search program nurtured with the Repbase repetitive sequence database and the RepeatMasker annotation tool. Findings: Unexpectedly, particular associations of transposable elements and ME/CFS epigenetic hallmarks were uncovered. A model for the disease emerged involving transcriptional induction of endogenous dormant transposons and structured cellular RNA interactions, triggering the activation of the innate immune system without a concomitant active infection. Implications: Repetitive sequence filters (ie, RepeatMasker) should be avoided when analyzing transcriptomic data to assess the potential participation of repetitive sequences ("junk repetitive DNA"), representing >45% of the human genome, in the onset and evolution of ME/CFS. In addition, transposable element screenings aimed at designing cost-effective, focused empirical assays that can confirm or disprove the suspected involvement of transposon transcriptional activation in this disease, following the pilot strategy presented here, will require databases gathering large ME/CFS epigenetic datasets.