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The emerging field of smart healthcare has identified emotion detection as a key component in improving patient care, diagnostics, and therapeutic interventions. This paper introduces an innovative approach to emotion detection within the healthcare domain by integrating a Convolutional Neural Network (CNN) with a Maximum A Posterior (MAP) estimator prepared for Magnitude-Squared Spectrum (MSS) analysis. The effectiveness of CNN’s advanced feature extraction capabilities with the statistical strength of MAP estimation offers a promising avenue for interpreting complex physiological signals. The proposed methodology aims to accurately discern and quantify emotional states, thus contributing to the personalization and effectiveness of healthcare services. To validate the efficacy of this approach, the work conducted extensive experiments on a diverse data set composed of physiological signals, demonstrating that the proposed model outperforms existing limitations in emotion recognition tasks. The integration of MSS into CNN frameworks, added with MAP estimation, provides a significant improvement in the detection and analysis of emotions, resulting in more responsive and intelligent healthcare systems. This proposed paper not only presents a novel methodological contribution, but also demonstrates the groundwork for future research toward the intersection of emotional intelligence and healthcare technology.