This research presents the design and implementation of an optimized smart shopping cart system targeted at improving efficiency and user experience in retail environments. The system was developed to address key challenges in existing smart cart technologies, such as high power consumption, moderate item recognition accuracy, and elevated false recognition rates. Results demonstrated that the proposed solution significantly outperformed these models across all metrics. A data visualization component was integrated to facilitate real-time monitoring and decision-making for both customers and retailers. This research contributes to the field of automated retail by providing a scalable, energy-efficient, and reliable smart cart solution. Future improvements have been suggested, including the integration of data analytics and personalized shopping recommendations to further elevate customer experience. The research highlights the potential of the proposed smart cart system to streamline retail operations and set new standards for technology-driven shopping experiences. By utilizing advanced sensors and efficient algorithms, the proposed model achieves enhanced performance metrics, including a reduced power consumption of 300 mWh, an item recognition accuracy of 95%, and a low false recognition rate of 3%.