Vaccine design and development against viral diseases is multifaceted. Classical vaccines (live-attenuated vaccines, inactivated vaccines, subunit, recombinant, polysaccharide, conjugate vaccines, and toxoid vaccines) are often less effective for many viral diseases. Hence, short peptide (10–20 amino acid residues) vaccine components exploiting T-cell mediated immunity have been recognized as alternative solutions. This involves the specific binding of short antigen peptides to allele (gene variant) specific host human leukocyte antigens (HLA). Allele-specific HLA typing among different ethnic groups has gained momentum in recent years through advances in sequencing (Next Generation Sequencing (NGS)), High Performance Computing (HPC), machine learning techniques such as Artificial Neural Networks (ANN) and Support Vector Machine (SVM) techniques. More than 20, 000 HLA alleles have been typed, defined, named, and made available at the IMGT®/HLA database (Immuno Polymorphism Database – ImMunoGeneTics Database/Human Leukocyte Antigen) for public access. Identification of short peptide antigens capable of binding specifically to the human host HLA alleles is now possible using computer-aided HLA-peptide binding prediction methods. This is achieved using three dimensional HLA structure based molecular modeling, and known HLA-peptide binding data enabled machine learning techniques like ANN and SVM. The former provides broad coverage across HLA alleles and the later offers high accuracy with high specificity for limited HLA alleles. Thus, the combined use of structural features, molecular modeling, machine learning techniques, and other applied mathematical models including Quantitative matrices (QM), Bayesian Networks (BN), and Hidden Markov Models (HMM) help in the effective design of short peptide vaccine components and immune therapeutics for the prevention and control of diseases caused by viruses. Hence, we outline recent advances in HLA-peptide binding prediction for short peptide vaccine design. © Springer Nature Switzerland AG 2019. All rights reserved.