Atherosclerosis remains a major contributor to cardiovascular morbidity and mortality worldwide. Key therapeutic approaches included lifestyle modifications, targeting inflammatory pathways, enhancing lipid management, and exploring immunotherapies. Recent advancements have highlighted the complexity of atherosclerosis, which involves complex processes such as inflammation, immune responses, and lipid metabolism. Computational modeling has emerged as a powerful tool in simulating these mechanisms, predicting disease progression, and testing potential treatment strategies. This enables the development of personalized therapies tailored specific for an individual patient. Structural bioinformatics further aids in drug discovery by identifying and validating therapeutic targets, while network-based computational approaches, which analyze protein-protein interactions, gene regulatory networks, and metabolic pathways, present innovative opportunities for drug design. Network analysis tools like STRING, Cytoscape, and DAVID are widely used for studying biological pathways and interactions. They help in identification of multiple targets. Using databases such as NCBI and STRING, researchers can uncover interactions between proteins and genes, perform cluster analysis, and predict potential therapeutic targets through gene ontology and pathway enrichment. This approach has proven useful in identifying disease mechanisms and drug development strategies for Atherosclerosis. Additionally, Atherosclerosis can also be addressed through different strategies such as: targeting lipid metabolism with drugs like statins and fibrates, targeting inflammation with anti-inflammatory therapies, and reducing oxidative stress using antioxidants such as vitamins and natural supplements like resveratrol and curcumin. These approaches aim to prevent plaque formation and reduce cardiovascular risk. In this chapter, we have discussed about targeting lipid metabolism using various enzymes and inhibitors such as HMG CoA reductase (HMGR) inhibitors, Anti- Proprotein convertase subtilisin/kexin type 9 (PCSK9), Peroxisome Proliferator-Activated Receptor (PPAR) α agonists, dual PPAR α and γ agonists, selective PPAR δ agonists and pan PPAR agonist, Cholesterol absorption inhibitors or Niemann-Pick C1-like 1 inhibitors, Adenosine triphosphate-citrate lyase (ACL) inhibitor, Angiopoietin like proteins (ANGPTL3) inhibitors, and Antisense oligonucleotide (ASO) therapies. Rational drug design for atherosclerosis involves structure-based and fragment-based approaches to develop selective compounds targeting proteins like HMGR, PCSK9, and PPAR. Structure-based drug design leverages 3D protein structures to analyze active sites, perform molecular docking, and optimize compounds for improved binding affinity. On the other hand, Fragment-based design screens small molecular fragments and optimizes them for binding. Both methods use databases like ZINC and PubChem to identify novel drug candidates for treating atherosclerosis.