Protocols for multiparty computation (MPC) enable a set of participants to engage and estimate a joint function of their secret inputs while disclosing only the output. MPC has a plethora of possible uses, including threshold cryptography, private DNA comparisons, privacy-preserving bidding, and private machine learning. Because of this, MPC has been the subject of extensive academic inquiry ever since Yao established it in the 1980s. Especially the evolution of privacy-preserving machine learning and the use of homomorphic encryption has given a significant push toward the development of MPC. In this research, we will look at what MPC is, which issues it resolves, and how it is currently employed as a privacy-preserving model.