Cervical cancer is the fourth most common cancer among women caused by mostly Human papillomavirus (HPV) infection. Considering its high incidence and mortality rate especially developing and least developed countries, it is essential to explore therapeutics against HPV infection. Preventive vaccines exist against HPV infections, which are the main cause of cervical cancer, still there is need for therapeutic vaccines for those already exposed to HPV. Immunotherapy has gained significant attention as a potential strategy which targets HPV oncoproteins. With advances in computational biology and bioinformatics, novel in silico tools have been designed to develop novel therapeutic vaccines in the field of immunotherapy. In this study, by performing computational biology approaches, it was aimed to identify potential B-cell and T-cell epitopes for vaccine targeting HPV 16, as one of the two types of strains (with HPV 18) causes approximately 70% of all cervical cancers. Both linear and discontinuous B-cell epitope predictions by using BepiPred, ABCpred and DiscoTope servers were applied. Additionally, NetMHCpan-4.1 and NetMHCIIpan-4.0 were used to predict MHC class I and MHC class II epitopes by utilizing Immune Epitope Database (IEDB), a manually compiled database of experimentally identified immune epitopes. The antigenicity prediction test was done to ensure that identified binding sites would effectively trigger the immune system. Our results suggest that computational biology approaches can be a useful tool for the design of epitope vaccines targeting HPV-16. However, further studies are necessary to validate our results and to determine the effectiveness of the vaccine in vitro and animal models.