Freezing of gait (FoG) prediction, combined with rhythmic laser cues, may help Parkinson’s disease (PD) patients overcome FoG episodes. This study aimed to utilize the impaired gait patterns preceding FoG to build a machine-learning-based model for FoG prediction. Acceleration signals were collected using an accelerometer attached to the lower back of 12 PD patients with FoG while they were performing designed FoG-provoking walking tasks. Step-based impaired gait features and conventional FoG detection features were extracted from the signals, based on which two FoG prediction models were built using AdaBoost to validate if the use of the impaired gait features can better predict FoG. For the correct labeling of the gait prior to FoG (pre-FoG), the personalized pre-FoG phase was defined according to the slope of the impaired gait pattern. The impaired gait features were relabeled based on the pre-FoG phase upon which the personalized labeled FoG prediction model was built. This was compared with the model built using unified labeling. Results showed that impaired gait features could better predict FoG than conventional FoG detection features with low time latency, and personalized labeling could further improve the FoG prediction accuracy. Using impaired gait features and personalized labeling, we built a FoG prediction model with 0.93 sec of latency. Compared to using conventional features and unified labeling, our model achieved 5.7% higher accuracy (82.7%) in patient-dependent test and 9.8% higher accuracy (77.9%) in patient-independent test. Therefore, using the impaired gait patterns is a promising approach to accurately predict FoG with low latency.