Diverse molecular networks have been extensively studied to discover therapeutic targets and repurpose approved drugs. However, it is necessary to select a suitable network since the performance of network medicine relies heavily on the completeness and characteristics of the selected network. Although a network using gene essentiality from cancer cells could be an effective platform for identifying anticancer targets, efforts to apply these networks in therapeutic applications have been limited. We constructed a phenotype-level network using the co-essentiality relationship between genes in CRISPR screens across 769 cancer cells to discover therapeutic targets for diverse cancer types. Leveraging cancer driver genes and network propagation on the networks, we found that the co-essentiality network better prioritized anticancer targets and biomarkers and predicted more precise drug responses in cancer cells than other molecular networks. The co-essentiality network outperformed conventional molecular networks in drug repurposing, which were validated in silico by clinical trial records. Notably, the co-essentiality network provided 30 repurposed drugs that the other networks have yet to cover, and we showcased three approved drugs repurposed for lung adenocarcinoma (atovaquone, eflornithine, and teriflunomide). Our study provides a novel network for precision oncology to improve the identification of therapeutic targets in specific cancers.