BACKGROUND: The occurrence rate of liver cancer is increasing in recent years. The significance of lactylation in tumor cells should not be neglected. This study aimed to discover a gene signature related to lactylation that can be used for diagnostic applications. METHODS: Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets. Differential analysis and random forest analysis were performed in hepatocellular carcinoma (HCC). Receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficiency, and logistic regression was established to obtain the risk score equation. Besides, the expression of genes in the signature was identified by TCGA database and verified by real-time quantitative polymerase chain reaction (RT-PCR). In order to analyze prognostic performance, patients diagnosed with HCC were stratified into either high risk score or low risk score categories based on the median risk score. In addition, immunotherapy and drug sensitivity of high risk score and low risk score groups were assessed. RESULTS: The prognostic performance of the diagnostic model was validated in the Gene Expression Omnibus (GEO) dataset. The patients with high risk score had worse outcomes than those with low risk score in the TCGA database. ROC curves showed the characteristics of lactylation-related gene signature with a good predictive capability. Furthermore, we developed a predictive nomogram for HCC patients, utilizing the data resources from TCGA. Finally, in terms of assessing the potency of immunotherapy in patients, the low risk score group had a lower Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating a good response to immunotherapy. CONCLUSIONS: Our identification of a gene model associated with lactylation presents a promising avenue for the development of targeted immunotherapeutic strategies.