There is a wide variety of birds worldwide, and identifying them by name and call requires time and effort. To get around this problem, there is a need for a robust model that can reliably identify the bird species from its name and call. The methodology is to create a robust and efficient system for identifying 510 unique bird species by preprocessing a dataset of bird images from eBird, iNaturalist, and a Convolutional Neural Network (CNN) to classify these images using the EfficientNetV2 architecture in TensorFlow. This approach includes collecting and processing raw data, building and training a model and delivering it using Flask. This research utilized 80% of the data for training and 20% for evaluating its performance. Deploying the model has led to an increase of 86% in the ability to recognize and forecast the class name of birds and the corresponding bird sound. The result displays the use of the algorithm to identify bird species from photographs and improve the accuracy and efficiency of monitoring bird populations and their habitats.