In Lima, Peru, minimarkets are vital, providing essential goods to a growing population. However, slow payment processes lead to long lines and frustrated customers, impacting satisfaction and profitability. The main issue is the slow, error-prone manual item scanning at the checkout. Addressing this inefficiency can enhance economic impact, customer satisfaction, and operational efficiency. Despite the benefits, implementing object detection technology faces challenges such as technological complexity, integration issues, diverse product ranges, and high costs. Previous solutions failed due to inadequate technology, high costs, poor integration, and user resistance. This paper proposes using YOLOv8, a state-of-the-art object detection model, for its precision, real-time processing, cost-effectiveness, and easy integration. This work includes custom hardware, an integration layer, and a user interface, with the aim of reducing checkout times, achieving over 94% product recognition accuracy, and improving customer satisfaction. Initial tests show promising results in speed, accuracy, and customer feedback.