Abstract Interleukin‐23 (IL‐23) is a dominant cytokine in psoriasis, a chronic inflammatory skin disease that severely diminishes patients' quality of life and reduces lifespan by up to 10 years. Despite therapeutic advancements, psoriasis remains a clinical challenge due to the absence of a definitive cure, treatment side effects, and substantial economic burden. Notably, no small molecule inhibitors (SMIs) directly targeting IL‐23 have been developed to date, leaving a critical gap in current therapies. In this study, SMIs emerged as promising therapeutic alternatives. A high‐throughput virtual screening of 1.57 million molecules was conducted, followed by molecular docking and molecular dynamics (MD) simulations (1, 10, and 100 ns) to identify potential candidates. Machine learning‐based binary QSAR models and MetaCore analysis established the relevance of these hits to psoriasis and other skin diseases. As a result, tenapanor (MM/GBSA score: −101.66 kcal/mol) and ChemBridge ID 7740 and ID 360118 (−101.59 kcal/mol, −91.003 kcal/mol) emerged as top candidates, demonstrating exceptional binding affinity and stability in 100‐ns simulations. These inhibitors represent promising therapeutic leads, offering an alternative to existing biologics. Further in vitro and in vivo validation will be essential to confirm their efficacy and potential as the first IL‐23‐targeted small molecule therapies.