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Diabetic foot ulcer (DFU) is a severe complication of diabetes, often leading to amputation due to poor wound healing and infection. The immune-related pathogenesis of DFU remains unclear, and therapeutic drugs are limited. This study aimed to explore the immune mechanisms of DFU and identify potential therapeutic drugs using machine learning and single-cell approaches. Through differential expression analysis of Gene Expression Omnibus (GEO) datasets, we identified 287 differentially expressed genes (DEGs), which were significantly enriched in IL-17 signaling and neutrophil chemotaxis pathways. Weighted gene co-expression network analysis (WGCNA) further pinpointed disease-associated modules containing 1,693 regulatory genes. Machine learning algorithms prioritized seven core genes—CCL20, CXCL13, FGFR2, FGFR3, PI3, PLA2G2A, and S100A8—with validation in an external dataset GSE147890 and single-cell sequencing revealing their predominant expression in neutrophils and keratinocytes. Immune infiltration analysis demonstrated significant dysregulation in DFU patients, characterized by elevated proportions of memory B cells, M0 macrophages, activated mast cells, and neutrophils. Potential therapeutic compounds were identified using the Connectivity Map database and tested through molecular docking and dynamics simulations. The study pinpointed selegiline, L-BSO, flunisolide, PP-30, and fluocinolone as promising therapeutic agents, offering new insights into the pathogenesis of diabetic foot ulcers (DFU) and potential therapeutic strategies.糖尿病足溃疡（Diabetic foot ulcer, DFU）是糖尿病的严重并发症之一，常因创面愈合不良与感染引发截肢。其免疫相关发病机制尚未明确，且治疗药物十分有限。本研究旨在借助机器学习与单细胞分析手段，探究糖尿病足溃疡的免疫机制并筛选潜在治疗药物。通过对基因表达综合（Gene Expression Omnibus, GEO）数据集进行差异表达分析，我们共鉴定出287个差异表达基因（differentially expressed genes, DEGs），这些基因显著富集于IL-17信号通路与中性粒细胞趋化通路。加权基因共表达网络分析（Weighted gene co-expression network analysis, WGCNA）进一步锁定了包含1693个调控基因的疾病相关模块。机器学习算法优先筛选出7个核心基因：CCL20、CXCL13、FGFR2、FGFR3、PI3、PLA2G2A及S100A8，并通过外部数据集GSE147890完成验证；单细胞测序结果显示，这些基因主要在中性粒细胞与角质形成细胞中表达。免疫浸润分析表明，糖尿病足溃疡患者体内免疫状态存在显著失调，表现为记忆B细胞、M0型巨噬细胞、活化肥大细胞及中性粒细胞比例升高。研究人员通过连通性图谱（Connectivity Map, CMap）数据库筛选出潜在治疗化合物，并借助分子对接与动力学模拟进行验证。最终确定司来吉兰（selegiline）、L-丁硫氨酸亚砜亚胺（L-BSO）、氟尼缩松（flunisolide）、PP-30及氟轻松（fluocinolone）为颇具前景的治疗药物，为糖尿病足溃疡的发病机制研究提供了新视角，同时也为其治疗策略提供了潜在方向。