This book explores how macrophage heterogeneity shapes the initiation and progression of atherosclerosis, integrating single-cell transcriptomic profiling with advanced AI-driven analytical frameworks. By mapping distinct macrophage subpopulations, their molecular signatures, and their interactions with immune checkpoints, the text provides a deeper understanding of inflammatory imbalance within arterial plaques. It highlights how machine-learning models refine cell-state classification, predict functional transitions, and reveal regulatory networks that remain hidden in conventional analyses. With a focus on translational relevance, the book demonstrates how integrating single-cell data with artificial intelligence enables more accurate identification of high-risk cellular phenotypes, improving diagnostic precision and pointing toward next-generation therapeutic strategies. It serves as a comprehensive resource for researchers seeking to combine immunology, cardiovascular biology, and computational approaches to uncover new opportunities for targeted intervention.