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Abstract Dysphagia or difficulty swallowing is caused by the failure of neurological pathways to properly activate swallowing muscles. Current electromyography (EMG) systems for dysphagia monitoring are bulky and rigid, limiting their potential for long‐term and unobtrusive use. To address this, a machine learning‐assisted wearable EMG system is presented, utilizing self‐adhesive, skin‐conformal, semi‐transparent, and robust ionic gel electrodes. The presented electrodes possess good conductivity, superior skin contact, and good transmittance, ensuring high‐fidelity EMG sensing without impeding daily activities. Moreover, the optimized material and structural designs ensure wearing comfort and conformable skin‐electrode contact, allowing for long‐term monitoring with high accuracy. Machine learning and mel‐frequency cepstral coefficient techniques are employed to classify swallowing events based on food types and volumes. Through an analysis of electrode placement on the chin and neck, the proposed system is able to effectively distinguish between different food types and water volumes using a small number of channels, making it suitable for continuous dysphagia monitoring. This work represents an advancement in machine learning assisted EMG systems for the classification and regression of swallowing events, paving the way for more efficient, unobtrusive, and long‐term dysphagia monitoring systems.