ADME genes are genes involved in drug absorption, distribution, metabolism, and excretion (ADME). Previous studies report that expression levels of ADME-related genes correlate with prognosis of hepatocellular carcinoma (HCC) patients. However, the role of ADME gene expression on HCC prognosis has not been fully explored. The present study sought to construct a prediction model using ADME-related genes for prognosis of HCC. Transcriptome and clinical data were retrieved from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC), which were used as training and validation cohorts, respectively. A prediction model was constructed using univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analysis. Patients were divided into high- and low-risk groups based on the median risk score. The predictive ability of the risk signature was estimated through bioinformatics analyses. Six ADME-related genes (CYP2C9, ABCB6, ABCC5, ADH4, DHRS13, and SLCO2A1) were used to construct the prediction model with a good predictive ability. Univariate and multivariate Cox regression analyses showed the risk signature was an independent predictor of overall survival (OS). A single-sample gene set enrichment analysis (ssGSEA) strategy showed a significant relationship between risk signature and immune status. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed differentially expressed genes (DEGs) in the high- and low-risk groups were enriched in biological process (BP) associated with metabolic and cell cycle pathways. A prediction model was constructed using six ADME-related genes for prediction of HCC prognosis. This signature can be used to improve HCC diagnosis, treatment, and prognosis in clinical use. © 2021 The Author(s).