Bioacoustics is an interdisciplinary area of science that deals with the study of sound production, transmission, and reception in biological organisms, providing essential information on animal behavior, biodiversity, and ecosystem health. Advanced signal processing methodologies have revolutionized research in the field of bioacoustics, allowing complex sound patterns to be analyzed in a more detailed and efficient way. This chapter will give an overview of the most recent signal processing methodologies applied to bioacoustic data, with a focus on how these methods are changing ecological studies. Some of the key techniques discussed involve time–frequency analysis methods, including spectrograms and wavelet transforms, which are very important in visualizing and interpreting complex acoustic signals. Noise reduction techniques and signal enhancement strategies have been explored for problems that arise from environmental noise in field recordings. This chapter further explores feature extraction processes and machine learning algorithms that underpin species identification and behavioral analysis. Advanced tools, such as deep learning models, are underlined, which can operate with large-scale datasets and extract bioacoustic signals with subtle patterns. Further applications are presented in environmental monitoring, wildlife conservation, and the study of the effects of human activity on natural soundscapes, highlighting the practical utility of the methods. Some of the future directions for this area are real-time acoustic monitoring and integration of IoT and edge computing. This chapter connects technological advancement with ecological needs in order to encourage innovative research and sustainable practices in bioacoustics. The insights provided herein illustrate how advanced signal processing might lead to game-changing outcomes in the understanding and preservation of the natural world.