For the labor-intensive and time-consuming task of identifying and classifying bird species, ornithologists and ecologists frequently rely on manual observation. Subjectivity in visual recognition and a narrow coverage in auditory data collecting are two drawbacks of conventional methods like field guides and acoustic monitoring. Furthermore, non-experts may find manual techniques difficult, and they might not offer current information on bird populations and the ecological relationships among them. We present a cutting-edge deep learning method that uses neural networks' ability to automatically recognize and categorize different bird species based on auditory and visual inputs in order to get around these limitations. Conventional bird identification uses field guides, which have limitations based on the user's skill level and might result in incorrect classifications because of seasonal variations in bird plumage. There are disadvantages to acoustic monitoring devices as well, such as the need for professional interpretation and the loss of visual clues. The accuracy and efficiency of bird population monitoring is limited by these labor-intensive and sometimes incomplete procedures. As a result, the identification and categorization of bird species require a more reliable and automated method. Convolutional Neural Networks (CNNs) are used in our suggested Deep Learning-based Approach for Bird Species Identification and Classification to analyze both visual and audio data. Large, annotated datasets of bird photos and audio recordings will be used by the system to train a deep learning model that will be able to accurately identify and categorize different bird species. In addition to identifying species, the model will take into account a number of other variables, including seasonal changes in feather color and bird sounds. Furthermore, our technology will enable real-time monitoring using field deployable hardware and mobile applications, giving immediate insights on bird populations and their activities. Our suggested solution would greatly improve the efficiency and accuracy of bird species identification and categorization by automating the procedure and utilizing deep learning. This will aid in the study of avian habitats and aid conservation efforts.