Bird species identification can offer crucial insights on changes in bird populations and habitats. Identification of bird species is vital for ecological research, biodiversity monitoring, and conservation projects. Conventional techniques for identifying birds primarily rely on visual observations, which can have limitations, particularly in densely forested areas and many ornithologists, researchers in the past encountered difficulties in identifying bird species and figuring out many patterns of bird species. The aim is to develop a dependable system for recognizing various bird species by fusing machine learning algorithms with audio recordings. The audio recordings in the datasets capture a wide variety of vocalizations. Spectrogram and feature extraction techniques are used to convert the unprocessed audio data into meaningful representations that can be used with machine learning models. Convolutional neural networks and supervised machine learning models are trained using these features. Accordingly, the survey provides different techniques and models with accurate predictions. Ornithologists, researchers, and wildlife enthusiasts will find this system useful in learning about the various bird species that occur in a particular location and the management of bird populations, which will support larger efforts in ecological research and biodiversity conservation.