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Atherosclerosis (AS) remains a leading cause of cardiovascular morbidity and mortality worldwide, with current treatments focused primarily on reducing low-density lipoprotein levels while failing to repair damaged endothelial cells, thus highlighting the urgent need for novel therapeutic drugs. To address this challenge, we analyzed human AS patient single-cell RNA sequencing datasets to identify disease-driving genes and then employed connectivity map analysis to screen for potential therapeutic compounds. Using network pharmacology and machine learning to predict core drug targets, the study validated drug-target interactions through molecular docking, molecular dynamics simulations, and surface plasmon resonance analysis, with experimental validation conducted using endothelial cell damage models and ApoE ^-/- atherosclerotic mice. The integrative approach successfully identified narciclasine as a promising therapeutic compound that targets vascular cell adhesion molecule 1 and intercellular adhesion molecule-1 (VCAM-1/ICAM-1) for AS treatment, with molecular studies confirming strong binding affinity and experimental validation demonstrating significant alleviation of endothelial dysfunction through downregulation of VCAM-1/ICAM-1 expression and reduction of aortic plaque burden in mouse models. This multiplatform methodology combining single-cell sequencing, network pharmacology, machine learning, computational simulation, and experimental validation provides a robust framework for drug discovery while positioning narciclasine as a promising therapeutic candidate warranting clinical investigation for AS treatment.