Pancreatic cancer is characterized by high malignancy and a lack of effective early diagnostic tools, resulting in most patients being diagnosed at an unresectable advanced stage, with a 5-year survival rate of approximately 13%. Traditional gemcitabine-based chemotherapy and single-targeted therapies (e.g., EGFR inhibitors, KRAS inhibitors) have shown limited efficacy, primarily due to the dense collagenous stroma and cancer-associated fibroblasts forming a drug-resistant barrier, as well as the high degree of tumor heterogeneity. Systems biology enables precise molecular subtyping through the integration of multi-omics data and, when combined with network pharmacology, facilitates the identification of multi-target combination strategies aimed at overcoming drug resistance and the immunosuppressive tumor microenvironment. Digital twin platforms can simulate tumor progression and drug responses in individual patients, thus guiding personalized therapeutic optimization. Clinical trials have demonstrated that KRAS G12D inhibitor MRTX1133, KRAS G12C inhibitors adagrasib and sotorasib, and the PARP inhibitor olaparib exhibit synergistic antitumor activity in specific molecular contexts. Multi-target combination strategies, in conjunction with artificial intelligence and real-time dynamic monitoring, hold promises for improving treatment efficacy and clinical outcomes in pancreatic cancer.