The goal of this project was to design a completely portable, continuous, real-time auscultation using a wearable stethoscope IoT system, which included soft material engineering, noise-reduction mechanisms, flexible mechanics, signal processing, and algorithm development. The soft auscultation technology is used for the first time to show continuous cardiopulmonary monitoring with numerous human subjects engaged in varied everyday activities. The computational mechanics study provides a crucial design guidance for creating a soft wearable device that can withstand bending and stretching while remaining mechanically reliable. Due to the stress distribution and conformable lamination, optimizing a system packaging utilizing biocompatible elastomers and soft adhesives enables for skin-friendly, durable adherence to the body while reducing motion artifacts. Even with the subject’s various motions, the soft gadget shows exact detection of high-quality cardiopulmonary sounds. The SWS, which uses a wavelet denoising method, outperforms market digital stethoscopes, as seen by the increased SNR for identifying four lung illnesses. The SWS and deep-learning integration exhibit a successful clinical research in which the soft stethoscope is utilized for continuous, wireless auscultation with many patients. Future research will focus on a large-scale clinical trial using the SWS to identify cardiopulmonary disorders automatically while delivering continuous, digital, real-time auscultation for the advancement of digital and smart healthcare. Integrating the SWS with additional sensing modalities, such as a very sensitive microphone, would broaden its uses for creating a next-generation biometric security system using individualized physiological signals.