Cancer is a multifactorial and heterogeneous disease characterized by complex genetic, epigenetic, and microenvironmental alterations that drive tumor initiation, progression, metastasis, and therapeutic resistance. Conventional "one drug-one target" strategies have demonstrated limited efficacy due to the intricate and interconnected nature of cancer signaling networks, tumor heterogeneity, and compensatory mechanisms that frequently lead to treatment failure and drug resistance. Consequently, cancer is increasingly recognized as a network disease involving dynamic interactions among genes, proteins, signaling pathways, and the tumor microenvironment. Network pharmacology has emerged as a promising systems-level approach that integrates systems biology, network science, multi-omics technologies, and computational modeling to elucidate these complex interactions. This review highlights the molecular basis of cancer, tumor biology, and the role of the tumor microenvironment in disease progression and therapy response. Furthermore, it discusses the significance of biological networks, including protein-protein interaction and gene regulatory networks, in understanding cancer pathogenesis. Particular emphasis is placed on the applications of network pharmacology in multi-target drug discovery, optimization of combination therapies, and personalized cancer treatment. The review also summarizes the contributions of genomics, proteomics, metabolomics, and key bioinformatics resources such as STRING, KEGG, Cytoscape, and TCGA in advancing precision oncology. Despite challenges related to data integration, standardization, and experimental validation, network pharmacology offers a comprehensive framework for deciphering cancer complexity and accelerating the development of safer, more effective, and personalized anticancer therapies.