The rapid development of immune checkpoint inhibitors has fundamentally changed the landscape of cancer treatment. These agents restore T cell-mediated antitumor immune responses by targeting key immune checkpoint molecules, thereby suppressing or eliminating tumors. However, their clinical application still faces multiple challenges, mainly including efficacy heterogeneity, drug resistance, immune-related adverse events. Furthermore, there is still a lack of reliable biomarkers for predicting efficacy and toxicity. More critically, there is absence of precise predictive models that can systematically integrate multiomics features, dynamic tumor microenvironment evolution, and patient individual differences to comprehensively address the above issues. This review systematically summarizes the latest advancements in this field. The main contents include emerging targets like lymphocyte activation gene 3, T cell immunoreceptor with immunoglobulin and tyrosine-based inhibitory motif domain, and mucin-domain-containing-3, combination strategies, and the current research status and limitations of various predictive biomarkers. Moreover, it focuses on the potential of microbiome regulation, metabolic reprogramming, and artificial intelligence-driven multiomics analysis technologies in achieving dynamic patient stratification and personalized treatment. By integrating the frontier research results and clinical insights, the review aims to provide a systematical theory framework and future directions for advancing precision immunotherapy.