BACKGROUND: Liver ischemia-reperfusion injury (LIRI) is a critical condition after liver transplantation. Understanding the role of immunogenic cell death (ICD) may provide insights into its diagnosis and potential therapeutic targets. METHODS: Differentially expressed genes (DEGs) between LIRI and normal samples were identified, and pathway enrichment analyses were performed, followed by immune infiltration assessment through the CIBERSORT method. The consensus clustering analysis was conducted to separate LIRI clusters and single-sample Gene Set Enrichment Analysis (ssGSEA) was used to analyze the distinct immune states between clusters. Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to identify hub genes associated with ICD. To establish diagnostic models, four machine learning techniques, including Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM), and Generalized Linear Models (GLM), were applied to filter gene sets. The receiver operating characteristic (ROC) curves were utilized to assess the performance of the models. RESULTS: Pathway enrichment results revealed significant involvement of cytokines and chemokines among DEGs of LIRI. Immune infiltration analysis indicated higher levels of specific immune functions in Cluster 2 compared to Cluster 1. WGCNA identified significant modules linked to LIRI with strong correlations between module membership and gene significance. The RF and SVM machine learning algorithms were finally chosen to construct the models. Both demonstrated high predictive accuracy for diagnosing LIRI not only in training cohort GSE151648 but also in validation cohorts GSE23649 and GSE15480. CONCLUSIONS: The study highlights the pivotal roles of ICD-related genes in LIRI, providing diagnosis models with potential clinical applications for early detection and intervention strategies against LIRI.