Climate change has reshaped the nature of our planet’s biodiversity, and accurate bird population data has continued to play an essential role in understanding the impact of climate change on avian populations. This case study demonstrated the feasibility of deploying a lightweight AI system for continuous, automated bird population monitoring, offering a valuable tool for ecological research and conservation efforts. The study utilized the North American Birds (NABirds) dataset to train various Convolutional Neural Network (CNN) architectures, including MobileNet and EfficientNet. The study explored different training strategies, including deep training and shallow training. Results indicated that EfficientNetB0 provided the best balance of accuracy, precision, and computational efficiency for edge deployment (Raspberry Pi), achieving a validation accuracy of 91.4% and a precision of 94.2% in the final experiments.