This study proposes an approach to enhance shelf-life management in retail shop settings through the utilization of sensors including Raspberry Pi, Python, Atmega256RFR2, and HC-05, integrated with mobile applications. The system aims to empower shopkeepers with real-time awareness of the shelf-life status of the foodstuff across different shelves. Temperature is the main factor affecting quality, but other factors like relative humidity and gas concentrations (mainly C2H4, O2, andCO2) also play an important role in maintaining the quality of the product. By leveraging sensor data and predictive analytics, the system can anticipate when the shelf life of a foodstuff is near expiration, reminding dynamic measures such as discounting or removal from the shelves. The integration of a mobile application provides users with convenient access to sensor readings and allows feedback mechanisms for continuous improvement. Through a user-centric design approach, the mobile application offers an inherent interface for monitoring shelf-life metrics and gathering input from shopkeepers, facilitating data-driven decision-making, and optimizing inventory management practices. This research contributes to reducing food wastage, enhancing operational efficiency, and fostering customer satisfaction in retail environments.