Wearables are becoming a tool to gather and analyze human health data. They can enable real-time insights, especially when acoustics and vibration sensing are combined with machine learning algorithms. This paper describes the development and application of wearable devices equipped with microphones, accelerometers, and gyroscopes for capturing acoustic signals and vibration of physiological activities. The paper also describes the signal processing methodologies, feature extraction, and deployment of machine learning algorithms such as Support Vector Machines (SVM), Neural Networks, and Decision Trees to analyze obtained data. The paper also describes how these technologies effectively monitor vital signs of Cardiovascular Diseases (CVD) or Respiratory Diseases. The results give wearable acoustic and vibration sensing systems unrealized potential for early disease detection, individual health recommendations, and performance optimizations.