Natural disasters like earthquakes, tsunamis, and volcanic eruptions are early identified by the unusual behavior of animals. By nature, animals have the sharp senses that allow them to detect the smell, change in air pressure, etc. Animals can give signals frequently for the disasters by exhibiting behaviors like unusual vocalization, restless behavior, and migration. Especially, the earthquakes are mainly identified by the elephant and birds which is responsible for the changes in environment. This chapter begins with an innovative approach for disaster prediction by analyzing the unusual behaviors of animals. Here, we are focusing on the vocalization and behavior of the animals by the changes of the nature. These changes of animal behaviors and sound can be detected by the artificial intelligence techniques. The early warning natural disaster systems are developed by using machine learning (ML) and deep learning (DL) algorithms. Introduction of the chapter explains about the drawbacks of traditional artificial intelligence-based detection and classification algorithms like decision tree, random forest, principal component analysis (PCA), hidden Markov model (HMM), and super vector machine (SVM). Mel frequency cepstral coefficients (MFCCs) are used to extract the acoustic features of animal sound. Above-mentioned traditional algorithms are used to classify the animal as either normal or abnormal behaviors. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid convolutional recurrent neural networks (CRNNs) are identifying the features of datasets. These algorithms are automatically extracting the key features from time-series sound recording data. Also clustering algorithms are employed to categorize the behavior patterns and integrating this algorithm with ML and DL to detect the anomalies when animals exhibit different behaviors such as restlessness, feeling, and sound. With the help of this techniques, we can detect the natural disasters in early stage, so that we can evacuate people from those surroundings. This chapter ends with the integration of machine learning (ML) and deep learning (DL) algorithm to detect behavioral anomalies in animals which offers immense possibilities for improving disaster management systems through enhanced accuracy.