Preterm birth (PTB) is the leading cause of death in twin pregnancies. A variety of parameters, such as cervical length, maternal medical history, demographics, and obstetric characteristics all have been shown to affect the risk of PTB. However, the relationship is not obvious. Early prediction of PTB in these pregnancies can assist physicians in identifying those patients who may benefit from preventive interventions and closer monitoring. This project aims to use machine learning to create an algorithm that predicts which twin pregnancy is at a risk of PTB.