A machine learning pipeline using multi-modal fetal MRI data predicts gestational age at birth with an R2 of 0.13 and a mean absolute error of 2.74 weeks. It achieves 0.77 accuracy, 0.59 sensitivity, and 0.82 specificity, with cervical length and placental T2* statistics as key features. This work presents a proof of concept for predicting preterm birth using MRI and machine learning.