The field of acoustic sensing is experiencing significant growth, with a focus on developing innovative systems for health and safety applications. Researchers are exploring the use of acoustic sensors on smartphones and smart speakers to detect various events, such as smoking, drowsy driving, and disease diagnosis. These systems utilize machine learning algorithms, including convolutional neural networks and long short-term memory networks, to analyze audio signals and achieve high accuracy in real-time. Notably, the use of acoustic sensing is enabling the development of personalized fitness monitoring systems and lightweight disease diagnosis models. The potential for clinical implementation and mobile device-based diagnostics is substantial, with implications for improving public health and safety. Noteworthy papers include: HearSmoking, which detects smoking events with an average total accuracy of 93.44 percent in real-time. HearFit+ achieves an average accuracy of 96.13% on fitness classification and 91% accuracy for user identification. D3-Guard detects drowsy driving actions with an average total accuracy of 93.31% in real-time. Detection of Disease on Nasal Breath Sound by New Lightweight Architecture achieves 97% accuracy in detecting COVID-19 from nasal breathing sounds.