This study sought to develop a novel diagnostic tool for atopic dermatitis (AD). Mouse transcriptome data were obtained via RNA-sequencing of dorsal skin tissues of CBA/J mice affected with contact hypersensitivity (induced by treatment with 1-chloro-2,4-dinitrobenzene) or brush stimulation-induced AD-like skin condition. Human transcriptome data were collected from German, Swedish, and American cohorts of AD patients from the Gene Expression Omnibus database. edgeR and SAM algorithms were used to analyze differentially expressed murine and human genes, respectively. The FAIME algorithm was then employed to assign pathway scores based on KEGG pathway database annotations. Numerous genes and pathways demonstrated similar dysregulation patterns in both the murine models and human AD. Upon integrating transcriptome information from both murine and human data, we identified 36 commonly dysregulated differentially expressed genes, which were designated as a 36-gene signature. A severity score (AD index) was applied to each human sample to assess the predictive power of the 36-gene AD signature. The diagnostic power and predictive accuracy of this signature were demonstrated for both AD severity and treatment outcomes in patients with AD. This genetic signature is expected to improve both AD diagnosis and targeted preclinical research.