Resting-state functional connectivity (RSFC) patterns of the human brain show unique inherent or intrinsic characteristics, similar to a fingerprint. There is significant interest in using RSFC to predict human behavior. Inspired by previous RSFC fingerprinting studies, we adopted whole-brain RSFC as discriminative features to predicted the MoCA scores in 102 individuals with T2DM, using a connectome-based predictive modeling (CPM). We find that, the identified CPM, based on whole-brain RSFC patterns, are strong for predicting the MoCA scores in T2DM. The application of CPM to predict neurocognitive abilities can complement conventional neurocognitive assessments and aid the management of people with T2DM.