Acoustic data plays a pivotal role in scientific and engineering research across various fields, including biology, communications, and Earth science. This study investigates recent advancements in acoustics, specifically focusing on machine learning (ML) and deep learning. ML, with its statistical techniques, autonomously identifies patterns in data. Unlike traditional acoustics, ML uncovers complex relationships among features and labels using extensive training data. Applying ML to acoustic phenomena like human speech and reverberation shows promising results. Additionally, this paper reviews acoustic signal processing for bowel sound analysis, emphasizing noise reduction, segmentation, feature extraction, and ML techniques. The integration of advanced signal processing and ML holds significant potential