Acoustic monitoring has gained extensive interest as an essential environmental tool for wildlife population calculation, biodiversity assessment, and conservation efforts. Several species uses regular vocalizations signals that are species specific, which enables monitoring via acoustic recognition even in isolated or difficult-to-access areas. Improvements in computerized sound recorders have empowered data collection at larger temporal and has given rise to enormous datasets. On the other hand, like the development of many data-driven methods, including camera traps and environmental DNA (eDNA), methods for acoustic data collection are progressing more rapidly than those for effective exploration and clarification. But, traditional Machine Learning (ML) methodologies frequently face difficulties with key challenges such as inadequate labeled datasets, the acoustic match between species and background noise which leads to inaccuracy in species classifications. This chapter discovers the usage of cutting-edge deep learning techniques to address these problems and improve the effectiveness of bioacoustic classification systems. This chapter begins with exploring the drawbacks of traditional bioacoustic classification techniques, such as k-Nearest Neighbor, Support Vector Machines (SVMs), Random Forests, Hidden Markov Models, and Gaussian Mixture Models which are frequently depend on handcrafted features and be unsuccessful to classify efficiently with the rising complexity and large volume of bioacoustic datasets. Deep learning techniques by contrast gives a more effective back ground for bioacoustic analysis by automatically learning features directly from or marginally processed data sets. This chapter highlights certain highly effective deep learning techniques used for bioacoustics classification, that includes Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), CNN-RNN Hybrids, Long Short-Term Memory Networks (LSTMs)-based models. This chapter ends with exploring the key benefits of deep learning techniques for improved bioacoustics classification, such as automated feature extraction, flexibility, scalability, and accuracy. These techniques have unlocked new way to natural world protection, biodiversity tracking, invasive species finding and environmental research.