Lung cancer remains a leading cause of cancer-related mortality worldwide, highlighting the need for multi-target therapeutic strategies. In this study, an integrated in silico approach combining molecular docking, pharmacokinetic profiling, and interaction analysis was used to identify potential multi-target inhibitors from a curated virtual library of 30 pyrazole, pyrimidine, chalcone, and indolinone derivatives retrieved from the PubChem database. The compounds were screened against seven lung cancer-related targets (epidermal growth factor receptor [EGFR], Kirsten rat sarcoma viral oncogene homolog, B-cell lymphoma 2, anaplastic lymphoma kinase protein, phosphoinositide 3-kinase, protein kinase B [AKT1], and mechanistic target of rapamycin). Docking results showed clear differences between parent scaffolds and optimized derivatives, with eight compounds exhibiting binding affinities below −6.0 kcal/mol across all targets. Among these, CID 317158, CID 637760, and CID 5367146 showed the most consistent and balanced performance. Absorption, distribution, metabolism, excretion, and toxicity analysis indicated favorable drug-like properties, including compliance with Lipinski’s Rule of Five, high gastrointestinal absorption, and acceptable toxicity profiles. Interaction analysis revealed stable binding across multiple targets, driven by hydrogen bonding, hydrophobic interactions, and π-mediated contacts among key residues, particularly within the EGFR and AKT1 kinase domains. Overall, CID 317158 and CID 637760 emerged as the most promising multi-target candidates, while CID 5367146 also demonstrated strong potential. These findings support the value of multi-target computational screening in early drug discovery and provide a basis for further experimental validation.