The proposed system aims to enhance the shopping experience, and this study demonstrates a smart trolley system for supermarkets that utilizes machine learning algorithms. The method of detection uses computer vision to recognize objects in real time, which allows for automatic item tracking. Aisle navigation is made easier by machine learning algorithms, which offer customized recommendations according to user preferences. The goal of the smart trolley to make shopping simpler, quicker, and more effective and offer an even more convenient and engaging experience. In addition to object recognition and item tracking, the smart trolley system is designed to improve the overall efficiency of the shopping process by reducing wait times at checkout counters. By automatically tallying items as they are added to the cart, the system can provide real-time billing, making the checkout process seamless. Furthermore, the integration of personalized offers based on user shopping habits enhances customer satisfaction, as shoppers receive recommendations tailored to their preferences, creating a more engaging and user-friendly experience in supermarkets.