Generally, the breathing process involves inhalation and exhalation in which the movement of air that occurs in the lungs causes an acoustic effect called lung sounds. These lung sounds can be breath sounds, adventitious sounds, and vocal resonance, which can be diagnosed based on the anatomy of the location, where the sounds are detected during physical examination. Moreover, it is essential to analyse the health condition of the respiratory system and the airway process without causing any harm to the patients using non-invasive methods. Examination through a stethoscope is a commonly followed method; however, it is quite difficult to analyse lung sounds with lower acoustic levels using existing stethoscopes. To overcome this issue, the digital electronic stethoscope is utilised nowadays. In this work, an artificial intelligence-based wearable digital stethoscope is designed and developed for the analysis of cough sounds. Furthermore, the Arduino Nicla voice-based edge computing board is utilised to acquire and analyse the cough sounds of abnormal patients. The Arduino Nicla Voice board has an inbuilt microphone and Inertial Measurement Unit (IMU) which is used to record acoustic, acceleration, and magnetometer signals from normal individuals and abnormal patients. Also, machine learning algorithms such as Random Forest and Decision Trees are adopted and deployed in the edge computing board to classify normal and abnormal cough sounds. Performance analysis parameters such as accuracy, precision, recall, and F1_Score are derived for the adopted machine learning classifiers to evaluate the efficiency and efficacy of the system. This work appears to be of high social relevance since the proposed work will assist in the early prediction of COVID-19 and similar, other diseases using cough sounds.