The increasing demand for advanced ornithological tools and techniques aimed at accurately identifying and classifying multiple bird species within a single-frame image has led us to develop a framework for researchers, environmentalists, and bird enthusiasts. Traditional methods often struggle to handle the complexity of diverse avian variants present in each scene, particularly when capturing images in real-world, uncontrolled environments. Single-frame images, such as those obtained through wildlife monitoring cameras or handheld devices, offer a practical and common means of avian observation. Our work centered on the automated detection and classification of a group of multi-variant bird species using a Deep Learning (DL) framework. The You Only Look Once (YOLO) version 5 networks were deployed in this study. Each object within the image frame was detected, annotated, and captured in a bounded box. Subsequently, each bounded object underwent scrutiny and recognition. The manually collected dataset comprises five mutually exclusive local bird species native to India. The developed model demonstrated satisfactory performance in terms of Precision, Recall, mAP, and F1 values, achieving scores of 0.87, 0.81, 0.82, and 0.826, respectively. This system can work as an automated bird species detection, recognition and classification tool for numerous individuals encountering difficulty recognizing multiple birds within a single image frame.