The article reviews the recent progress and potential biomarkers in the field of immune checkpoint inhibitors (ICIs) for cancer treatment. ICIs, particularly PD-1/PD-L1 and CTLA-4 inhibitors, have shown promising therapeutic outcomes in various cancers, but their efficacy is limited by low response rates and immune-related adverse events (irAEs). The development of predictive biomarkers is crucial to identify patients who will benefit from ICIs and to minimize irAEs. The review discusses various predictive biomarkers, including immune cell infiltration, PD-L1 overexpression, neoantigens, genetic and epigenetic signatures. Immune cells, such as TILs and peripheral blood lymphocytes, are important predictors of response to ICIs. PD-L1 overexpression is a common biomarker, but its predictive value is not universal. Neoantigens, which are mutations that generate new antigens, can predict both response and resistance to ICIs. Genetic and epigenetic signatures, such as mutational load and mismatch repair deficiency, also play significant roles in predicting treatment outcomes. The article emphasizes the need for further research to develop reliable biomarkers and integrate them into clinical decision-making to optimize patient selection and improve treatment outcomes.The article reviews the recent progress and potential biomarkers in the field of immune checkpoint inhibitors (ICIs) for cancer treatment. ICIs, particularly PD-1/PD-L1 and CTLA-4 inhibitors, have shown promising therapeutic outcomes in various cancers, but their efficacy is limited by low response rates and immune-related adverse events (irAEs). The development of predictive biomarkers is crucial to identify patients who will benefit from ICIs and to minimize irAEs. The review discusses various predictive biomarkers, including immune cell infiltration, PD-L1 overexpression, neoantigens, genetic and epigenetic signatures. Immune cells, such as TILs and peripheral blood lymphocytes, are important predictors of response to ICIs. PD-L1 overexpression is a common biomarker, but its predictive value is not universal. Neoantigens, which are mutations that generate new antigens, can predict both response and resistance to ICIs. Genetic and epigenetic signatures, such as mutational load and mismatch repair deficiency, also play significant roles in predicting treatment outcomes. The article emphasizes the need for further research to develop reliable biomarkers and integrate them into clinical decision-making to optimize patient selection and improve treatment outcomes.