<p>Research on novel treatment approaches is crucial for lung cancer, because it is one of the most common and aggressive malignancies with high mortality in the world. The potential advantages of using in silico methods to find phytochemicals for lung cancer treatment have been summarized in this review. This also highlights the various computational tools, such as ADMET profiling, network pharmacology, molecular docking, and machine learning algorithms, involved in the identification of potential phyto-constituents for lung cancer therapy. Key molecular targets that were assessed against a range of phytochemicals that showed multi-target binding and promising pharmacokinetic properties included EGFR, KRAS, ALK, and PD-L1. Prominent compounds such as quercetin, curcumin, and luteolin showed notable interactions with carcinogenic pathways, tumor microenvironment modulators, and apoptosis inducers. Mechanistic understanding, high-throughput screening, and economical drug development were made possible by this computational approach. This review demonstrates the potential utility of in silico techniques to bridge the gap between precision oncology and traditional medicine, as well as the intriguing function of phytochemicals as supplemental medicines in lung cancer therapy.</p>