Acoustic stethoscopes, pivotal in modern medicine, allow clinicians to hear the internal sounds of a patient's body, particularly the heart and lungs, offering critical insights for diagnosing numerous conditions. This research delineates the development and potential applications of innovative, flexible, and adaptable soft wearable stethoscopes. Traditional acoustic stethoscopes, despite their wide utility, are limited by the need for extensive clinical expertise and optimal hearing capabilities in practitioners. Moreover, their accuracy in capturing the subtle sounds of the cardiopulmonary systems is severely depends on user proficiency. Addressing these challenges, this dissertation introduces wireless soft wearables equipped with advanced acoustical sensing capabilities. These devices not only facilitate self-diagnosis of diseases using machine learning but also showcase promise in biometric security by using unique body sounds as identifiers. The wearables, designed for continuous, real-time auscultation, can be employed across various diseases, transcending the limitations of traditional stethoscopes. The main innovation lies in the soft wearable system's adaptability and broad application spectrum, underscoring its potential to revolutionize modern medical diagnostics and further with security protocols.