This project introduces a Grocery Store Object Detector, developed using YOLOv8s, designed to identify grocery store items in real-time. The model detects 24 predefined grocery products using a robust object detection framework. It is hosted locally via a Flask-based web interface, where users can upload images to detect and return identified objects. The project aims to simplify the checkout process in grocery stores, potentially allowing store employees to scan and bill entire carts efficiently using their smartphones. The dataset for this project was sourced from Roboflow, with extensive training and testing to ensure accuracy. Experimental results validate the model's capability to identify products effectively, laying the foundation for future deployment in real-world retail environments.