Bioacoustics has rapidly grown into a key branch of science that investigates all types of acoustic signals from biological systems, enabling novel research concerning the monitoring of wildlife and environmental health for the purpose of species conservation. Transformation of raw acoustic data into meaningful information requires robust techniques of feature engineering, which bridge signal acquisition to actionable insights. This chapter reviews principles, methods, and applications of bioacoustic feature engineering under an emphasis on unlocking the mysteries of biological communication and soundscapes. We review time-domain, frequency-domain, and time–frequency features of extraction, focusing on their biological relevance and computational efficiency. Concrete techniques like MFCC, spectral centroid, and wavelet analysis will be studied along with the state-of-the-art techniques that embody deep learning frameworks for the automatic extraction of features. Other challenges are related to noise interference, scalability for large datasets, and cross-species generalization. This chapter goes on to describe case studies in various applications-avian song analysis, marine mammal monitoring, insect sound identification, among others-to illustrate the transformative potential of bioacoustic data within ecological research and conservation. This chapter synthesizes current methodologies and emerging trends to provide a comprehensive guide for researchers and practitioners in the advancement of bioacoustic feature engineering.