This project introduces an advanced framework for automated bird species identification, leveraging audio analysis and machine learning. By extracting key features from avian calls, it achieves high accuracy in recognizing different species. The adaptable framework works in diverse environments, enabling real-time processing for field researchers and conservationists. Integrated with deep neural networks, it continuously improves accuracy and fosters collaboration through open-source development. In summary, this project significantly advances ornithological research and contributes to global conservation efforts.