Deep learning has significantly advanced object detection and relationship inference inside images in recent years, revolutionizing the field of computer vision. The paper aims to implement object detection using the YOLOv7 model, which is a real-time object detection framework, along with the OpenCV library. The YOLO model is pre-trained on a large dataset and can detect a variety of objects in the input images. This work uses the YOLOv7 model, class labels, and an input image through a user interface-based application. The paper further aims to provide information about the detected object and provide the logical relationship between the detected objects through Bard API and WIKIPEDIA API. This work intends to shed light on the transformative effect of deep learning in improving object recognition and relationship inference, ultimately encouraging a better comprehension of complex visual situations by outlining the essential approaches and demonstrating their practical impact in Education 5.0. The study compares ChatGPT and Bard API based on parameters related to educational environments. With an astounding 5–120 FPS speed and an average precision of 56.8%, the model in use offers exceptional performance.