Background: Freezing of gait (FoG) is a common and debilitating symptom in individuals with advanced Parkinson’s disease (PD), significantly increasing the risk of falls. Wearable devices have facilitated the detection of FoG and falls, but early prediction remains underexplored. This study investigates the use of multimodal sensor fusion and deep learning for the early prediction of FoG events in PD patients. Research Question: Can a multimodal sensor fusion deep learning model accurately predict FoG events well before time in Parkinson’s disease patients, and how robust is the model to noise and inter-subject variability? Methods: The proposed study utilized Inertial Measurement Unit (IMU), Electromyography (EMG), and Electroencephalography (EEG) signals from PD patients to develop and evaluate deep learning models. The CNN+LSTM architecture was employed and compared with other classifiers. Stratified ten-fold cross-validation was used to assess model accuracy. The robustness of IMU+EMG and IMU+EMG+EEG configurations to noise was tested, and inter-subject performance evaluation was conducted. Pre-FOG detection capabilities were also analyzed to emphasize the importance of temporal dynamics in the multimodal approach. Results: The CNN+LSTM model achieved a high accuracy of 94.45% in predicting FoG events. The IMU+EMG and IMU+EMG+EEG configurations demonstrated robust performance across inter-subject evaluations. The models showed resilience to noise, with the CNN+LSTM and IMU+EMG+EEG configurations maintaining high accuracy. Pre-FOG detection achieved 94.20% accuracy, highlighting the model’s effectiveness in capturing temporal dynamics. Significance: The CNN+LSTM model, particularly in the IMU+EMG+EEG configuration, proves to be a robust and accurate predictor of FoG events in PD patients. The study’s findings underscore the potential clinical impact of multimodal sensor fusion and deep learning in reducing false positives and negatives and enhancing precision, sensitivity, and specificity. These insights are crucial for deploying reliable FoG prediction systems in real-world settings and advancing PD management. Future research should explore additional sensor modalities, transferability to different PD cohorts, longitudinal data, and real-time deployment in clinical environments.