Background: Endothelial activation is a recognized histological feature of atopic dermatitis (AD), but its transcriptional programs remain incompletely characterized. Methods: We reanalyzed public single-cell RNA-seq data from the skin of five patients with AD and four healthy donors using AD-associated gene set scoring, CellChat, endothelial hdWGCNA and pseudotime ordering, and examined candidate genes with eight machine learning classifiers and SHAP in public bulk transcriptomes of lesional and non-lesional AD skin. Results: Inferred intercellular interactions were more numerous in AD (1348 vs. 1111), with COLLAGEN, CD99 and MIF showing the largest gains in communication strength. Differences in AD-associated gene set scores between AD and healthy endothelial cells were small and inconsistent between scoring approaches. Among seven endothelial modules, Endo7, an NF-κB-responsive immediate-early module containing SELE, ICAM1, IRF1 and NFKBIA, overlapped most strongly with the AD gene set (10 genes; approximately 30-fold enrichment). AD endothelial cells were concentrated at intermediate pseudotime positions (Cohen’s d = 0.334). In bulk skin, classifiers based on 14 LASSO-selected genes separated lesional from non-lesional AD skin in a small test set (AUC 0.70–0.98; likely optimistic estimates, as feature selection preceded the training–test split and paired samples from the same patients may span both partitions), with SELE, TOP2A and IRF1 ranked highest by SHAP. Conclusions: This hypothesis-generating analysis identifies AD-associated endothelial activation signatures and candidate genes that require confirmation in independent, clinically characterized cohorts and experimental studies.