<p>Histone deacetylases (HDACs) are important epigenetic regulators in gene expression, chromatin remodelling, and protein stability. When HDACs are not adequately regulated, they can contribute to various diseases, especially cancer and neurodegenerative disorders, which makes them promising targets for therapy. Computational methods have been essential in identifying and optimizing HDAC inhibitors by shedding light on their binding mechanisms, selectivity, and effectiveness. Techniques like molecular docking, including rigid and flexible docking, have been widely used to predict how ligands interact with HDAC catalytic sites, focusing on zinc coordination and key active site residues. Molecular dynamics (MD) simulations help refine these predictions by evaluating the stability and flexibility of protein–ligand complexes in physiological conditions. Moreover, machine learning and deep learning algorithms have improved high-throughput virtual screening (HTVS) by analyzing extensive datasets to more accurately predict inhibitor activity and potential off-target effects. Structure-based drug design (SBDD) and homology modeling have also advanced the study of HDAC isoforms that lack high-resolution crystal structures, allowing for the creation of selective inhibitors that have minimal toxicity. Additionally, bioinformatics tools have played a significant role by integrating genomic and transcriptomic data to evaluate HDAC expression patterns and their involvement in disease progression. This review thoroughly examines the progress in computational HDAC research, highlighting the collaboration between in silico techniques and experimental validation. By utilizing artificial intelligence, network pharmacology, and cheminformatics, researchers can expedite the discovery of next-generation HDAC inhibitors that offer enhanced efficacy, specificity, and therapeutic potential.</p>