Computational bioacoustics is using the source of artificial intelligence to detect and classify the animal behavior, interactions, and vocalization. Signal processing and analysis play an important role in study of bioacoustics. Various machine learning (ML) and deep learning (DL) classification algorithms are explained in this chapter. At the beginning of the chapter, it aims to provide an overview of the traditional ML and DL algorithms for computational bioacoustics and clarification of concepts. To analyze audio signals for species detection, conventional classification algorithms like support vector machines (SVM), Hidden Markov Models (HMM), k-nearest neighbors (KNN), decision trees, and random forest are used. To improve the accuracy of the large dataset, the features are extracted from raw audio data using deep learning algorithms like convolutional neural networks (CNNs), recurrent neural networks (RNNs) and CNN-RNN Hybrids, long short-term memory networks (LSTMs) with different architectures ResNet, Inception. Pre-trained audio neural networks (PANNs) are trained on a large-scale dataset called AudioSet can be used as an architecture for animal sound detection. The classification of bioacoustics sounds using artificial intelligence follows several key steps. Preprocessing is a crucial stage, where raw audio data is prepared for analysis and classification. Noise reduction techniques are applied to remove common background sounds like wind, rain, and human activity from the recordings. Data augmentation is used to artificially increase the size of the dataset by the software packages of python. Acoustics analysis made by mel-frequency cepstral coefficients (MFCCs) is used to compressing the spectral information into a small number of standardized measurements. Nowadays CNN architectures like WaveNet replaced the MFCCs to detect the animal sounds accurately. This chapter ends with comparing all the traditional algorithms for the computational bioacoustics and provide the best classification algorithm based on the accuracy of the extracted features. The key benefits of this chapter include traditional ML and DL techniques for classification of bioacoustics, biodiversity protection, different animal species identification.