In hepatocellular carcinoma (HCC) individuals, the influence of numerous variables on the HCC prognosis has gained widespread recognition. Nevertheless, there remains a need for further elucidation regarding the underlying mechanism of PANoptosis-related genes (PRGs) on HCC. A consensus clustering approach, based on the TCGA-LIHC data, was used to identify specific subtypes linked to PANoptosis in this study. Next, a signature consisting of predictive differentially expressed genes for these subtypes was established using a least absolute shrinkage and selection operator (LASSO) regression analysis. Additionally, the reliability of the signature was confirmed through verification investigations using the data from the ICGC database and TCGA-LIHC. In the end, we developed a nomogram to enhance the clinical effectiveness of our prediction tool. PRG signature in this study has been highly related to the prognosis of individuals diagnosed with HCC, which was established with six genes. Also, this signature and clinicopathological features were put together to create a nomogram. Interestingly, the forecasting efficiency of this combination approach is better than other prediction models in the reported literature. In addition, an examination of the immunological surroundings indicates that the group with low risk exhibited elevated ESTIMATE score, ImmuneScores, and StromalScores. More, significant differences in infiltrating immune cells and the expression levels of immune-related genes were found between the two groups. In HCC patients, the PRG signature exhibits potential as a biomarker, offering a significant point of reference for tailoring individual therapy.