In this study, we present a reliable CNN-based Efficient-Shuffle-Net (EffShuffNet) model for bird species recognition. Our method starts with an input image of a bird, which is preprocessed and has its features extracted using a CNN model that has already been trained. This model is good at capturing small characteristics, such as textures and forms, which are important for distinguishing different kinds of birds. On our fine-grained image classification studies, we achieve remarkable training accuracy of 97.54% and validation accuracy of 95.58% by utilizing generic stem transfer learning approaches and optimizing EfficientNet. Our results show that DL models outperform conventional techniques for classifying bird species, which holds promise for improvements in environmental stewardship and biodiversity monitoring.