This paper presents a comprehensive, data-driven investigation into the physiological and psychological benefits of yoga, leveraging advanced machine learning techniques and multimodal biosignal data. The proposed system integrates five distinct types of biosensors—EEG, ECG, EMG, GSR, and \({\textbf {SpO}}_{{\textbf {2}}}\) —implemented through custom-designed wearable hardware to capture detailed physiological responses. To facilitate robust model training, a hybrid dataset was constructed by combining the WESAD dataset, IEEE Health Sensor Data, and a Kaggle EEG-ECG Stress Dataset. These datasets were harmonized using transfer learning techniques and feature alignment strategies. Following thorough preprocessing and feature engineering, we trained a deep learning-based regression model using a multi-layer perceptron (MLP) architecture to predict health improvements resulting from regular yoga practice. Experimental results demonstrate quantifiable enhancements in several physiological health indicators, thereby offering objective validation of yoga’s benefits through biomedical signal analysis.