In multi jet fusion process, the thermal history varies at different locations inside the printing chamber resulting in the dependence of crystallinities of the printed parts. As performing experimental test is time consuming and costly, it is desirable to have the crystallinity be predicted even before the parts are printed. Thus, this work presents a crystallinity prediction method based on machine learning for MJF-printed polyamide 12. In the model, the predicted thermal profiles and the experimental measurements of crystallinities were employed to train and optimize the machine learning regression model. The prediction results explain the formation of crystallinity is significantly affected by the duration of first cooling stage, temperature at the end of printing process, the duration of extremely low cooling rate, and the cooling condition of the second cooling stage. Additionally, an optimized Ridge regression model has been found to predict the crystallinity with the accuracy of 93.6 %. This study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects ( IAF-ICP ) Funding Initiative, as well as cash and in-kind contribution from the industry partner, HP Inc.