The degree of crystallinity of a polymer is a critical parameter that controls a variety of polymer properties. A high degree of crystallinity is associated with excellent mechanical properties crucial for high-performing applications like composites. Low crystallinity promotes ion and gas mobility critical for battery and membrane applications. Experimental determination of the crystallinity for new polymers is time and cost intensive. A data-driven machine learning-based method capable of rapidly predicting the crystallinity could counter these disadvantages and be used to screen polymers for a myriad of applications in a fast, inexpensive fashion. In this work, we developed the first-of-its-kind, data-driven machine learning model to predict the most-likely polymer crystallinity trained on experimental data and theoretical group contribution methods. Since polymer data under consistent processing conditions are unavailable, we tackled process variability by using the "most-likely" polymer values which we refer to as the polymer's tendency to crystallize. Experimental data for polymers' tendency to crystallize is limited by number and diversity, and to tackle this, we augmented experimentation-based data with data using group contribution methods. Therefore, this work utilized two data sets, viz., a high-fidelity, experimental data set for 107 polymers and a more diverse, less accurate low-fidelity data set for 429 polymers which used group contribution methods. We used a multifidelity information fusion strategy to utilize all the information captured in the low-fidelity data set while still predicting at the high-fidelity accuracy. Although this model inherently assumed "typical" processing conditions and estimated the "most-likely" percent crystallinity value, it can help in the estimation of a polymer's tendency to crystallize in a far more cost-effective and efficient manner.