INTRODUCTION: Asthma has an annual increasing morbidity rate and imposes a heavy social burden on public healthcare systems. The aim of this study was to use machine learning to identify asthma-specific genes for the prediction and diagnosis of asthma. METHODS: Differentially expressed genes (DEGs) related to asthma were identified by examining public sequencing data from the Gene Expression Omnibus, coupled with the support vector machine recursive feature elimination and least absolute shrinkage and selection operator regression model. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis and correlation analyses between gene and immune cell levels were performed. An ovalbumin-induced asthma mouse model was established, and eukaryotic reference transcriptome high-throughput sequencing was performed to identify genes expressed in mouse lung tissues. RESULTS: Thirteen specific asthma genes were obtained from our dataset analysis ( DISCUSSION: This study identified new marker genes for the prediction and diagnosis of asthma, which can be further validated and applied clinically.