Objective  To screen the key characteristic genes of metastatic nasopharyngeal carcinoma (mNPC) and analyze the immune cell infiltration in tumor microenvironment using machine learning algorithm. Methods   Firstly, the training set GSE103611 was downloaded from the GEO database, and the data were subjected to differential expression gene (DEGs) screening, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genome (KEGG) and immune cell infiltration analysis. Second, the predicted genes in DEGs were screened by least absolute shrinkage and selector operation( LASSO) regression, and the characteristic genes were screened by using the expression level of the predicted genes and receiver operating characteristic(ROC). Third, the correlation between characteristic genes and immune cells was further analyzed to determine the key characteristic genes. Finally, the expression levels of key characteristic genes and ROC were verified using the reverse validation set GSE1245 data. Results  A total of 136 DEGs were obtained, and their KEGG were mainly enriched in cytochrome P450, tumor necrosis factor(TNF) signaling pathway, prion disease, EB virus infection, and other pathways. GO was mainly enriched in the negative regulatory processes of peptide-based tyrosine phosphorylation modification, viral gene expression, and B cell and leukocyte activation. The difference in the degree of infiltration of the 22 immune cells in nasopharyngeal carcinoma(NPC) and mNPC was not significant. Two key characteristic genes (DAZ1 and SCCPDH) of mNPC were finally obtained by LASSO regression, and they were significantly correlated with immune cells in the mNPC microenvironment (P<0.05). In the reverse validation data set, the differential expressions of DAZ1 and SCCPDH between non\|NPC(nNPC) and NPC groups were not significant (P>0.05), and the AUC values of ROC of both were less than 0.6. Conclusion   DAZ1 and SCCPDH are the key characteristic genes of mNPC and can be used as important markers for mNPC and immunotherapy.