Patient sentiment analysis is crucial for identifying issues, facilitating timely interventions, and enhancing healthcare quality. The management and analysis of pain are interconnected for delivering enhanced care. Self-assessed pain level evaluation is important in an intelligent healthcare framework for determining the most effective treatment. This study proposes an effective pain sentiment identification system based on analyzing patients’ facial expressions and EEG signals, considering diverse psycho-physiological traits in an efficient mobile healthcare system. The proposed system operates in four phases: (i) Patient facial regions and physiological dispositions are captured in the form of EEG signals. (ii) Extracted facial regions and EEG signals are analyzed through separate deep learning techniques to identify key features reflecting pain levels. (iii) Advanced feature tuning and representation techniques distinguish between low and high pain levels using deep learning models. (iv) Score level fusion enhances the performance of deep pain identification techniques within the architecture. The system’s performance was evaluated using the BioVid Dataset, and the results were compared to some existing well-known methodologies. Our proposed method has been rigorously evaluated through extensive experiments and has been shown to significantly outperform other state-of-the-art systems by 5% for use in smart healthcare frameworks.