One of the ongoing challenges in avian ecology is tracking and monitoring bird populations, especially in real-time situations. Historically, counting birds reliably and quickly has proven to be complicated. Predicting bird populations in different areas by examining bird footage presents a viable solution to this problem. To achieve this, the suggested model uses deep learning’s computing power, a prominent area of artificial intelligence known for its proficiency in image processing. The suggested model uses neural network-based feature extraction to provide state-of-the-art, generic bird detection. Fast R-CNN (Region-based Convolutional Neural Network) provides the bird-bounding boxes for counting in real-time with the help of anchor boxes containing the detected bird. The counting will be done based on the in-out of the threshold line set in the frame’s ROI (Region of Interest). Such a video-based bird counting approach uses deep learning to enable real-time bird census solutions and democratizes ornithological research through citizen scientist participation. In summary, this model demonstrates that real-time, video-based bird counting represents a pivotal advancement in avian ecology, with far-reaching implications for the conservation and understanding of bird populations.