Birds play an essential role in maintaining ecological balance, and the accurate identification of bird species is crucial for biodiversity monitoring. Traditional identification through physical characteristics and vocalizations is often hindered by the variability in appearance and complex acoustic patterns. These days advances in machine learning, particularly deep neural networks (DNNs), have significantly improved the performance of audio-based classification systems. This study presents an overview of various methods used for of bird sound classification techniques, emphasizing spectrogram-based analysis, feature extraction methods, such as MFCC, and the integration of deep learning models. This survey aims to support future research efforts in developing reliable and scalable bird sound identification systems.