DNA methylation (DNAm) profiles in central airway epithelial cells (AECs) may play a key role in pathological processes in asthma. The goal of the current study is to compare the diagnostic performance of DNAm markers across three tissues: AECs, nasal epithelial cells (NECs), and peripheral blood mononuclear cells (PBMCs). Additionally, we focused on the results using the machine learning algorithm in the context of multi-locus effects to evaluate the diagnostic performance of the optimal subset of CpG sites. We obtained 74 subjects with asthma and 41 controls from AECs, 15 subjects with asthma and 14 controls from NECs, 697 subjects with asthma and 97 controls from PBMCs. Epigenome-wide DNA methylation levels in AECs, NECs and PBMCs were measured using the Infinium Human Methylation 450K BeadChip. Overlap analysis across the three different sample sources at the locus and pathway levels were studied to investigate shared or unique pathophysiological processes of asthma across tissues. Using the top 100 asthma-associated methylation markers as classifiers from each dataset, we found that both AEC- and NEC-based DNAm signatures exerted a lower classification error than the PBMC-based DNAm markers (p-value = 0.0002). The area-under-the-curve (AUC) analysis based on out-of-bag errors using the random forest classification algorithm revealed that PBMC-, NEC-, and AEC-based methylation data yielded 31 loci (AUC: 0.87), 8 loci (AUC: 0.99), and 4 loci (AUC: 0.97) from each optimal subset of tissue-specific markers, respectively. We also discovered the locus-locus interaction of DNAm levels of the CDH6 gene and RAPGEF3 gene might interact with each other to jointly predict the risk of asthma - which suggests the pivotal role of cell-cell junction in the pathological changes of asthma. Both AECs and NECs might provide better diagnostic accuracy and efficacy levels than PBMCs. Further research is warranted to evaluate how these tissue-specific DNAm markers classify and predict asthma risk.