The integration of electromyography (EMG) signals into biometric recognition has garnered significant attention due to their potential for highly secure and reliable identification. Unlike vision-based methods like cameras, EMG is immune to lighting conditions, clothing, or occlusions. This study presents a hierarchical cascade deep learning framework aimed at simultaneously performing hand gesture recognition and subject-specific biometric classification. Utilizing the publicly available Gesture Recognition and Biometrics ElectroMyogram (GRABMyo) dataset, which encompasses diverse EMG recordings from 43 individuals performing 17 unique gestures, this study proposes a two-staged classification approach. The first stage concentrates on recognizing the hand gesture, succeeded by a gesture-specific model that subsequently categorizes the subject associated with the identified gesture. The experimental results demonstrate the effectiveness of the proposed model, which achieved an average accuracy of 71.62% across gesture and subject classification, representing an improvement of approximately 5% and 21% compared to conventional single-model and multi-task strategies evaluated in this study, highlighting this approach’s effectiveness in handling the variability of EMG signals across different gestures and subjects. The findings underscore the potential of the proposed methodology for enhancing EMG-based biometric recognition systems.