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Birds are a diverse class of warm-blooded creatures, with around 10,000 living species presenting a range of characteristics and appearances. Although though individuals frequently enjoy viewing birds, accurate bird species identification requires an understanding of the field of ornithology. To address this issue, we offer a CNN-based automated model that can distinguish between several bird species using a test dataset. Our model was trained using a dataset of 7,637 pictures representing 20 distinct bird species, of which 1,853 were selected for testing. The deep neural network's design was developed to analyse the images and draw out traits for categorization. We tested a variety of hyperparameters and techniques, such data augmentation, to improve performance. According to our findings, the suggested model evaluated on the dataset had a promising accuracy of 98%. Our study also emphasises the value of utilising technology to safeguard and maintain endangered bird populations as well as the promise of convolutional neural networks for bird species identification. In summary, the suggested methodology can help with bird population identification and tracking, which will ultimately help with their preservation and protection. The model's accuracy may be increased, and its application can be broadened to cover other bird species