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Food safety and quality monitoring have become critical concerns in recent years, as improper storage and delayed detection of spoilage lead to health risks and increased food wastage. Traditional methods of checking food freshness rely on manual inspection such as smell and appearance, which are often inaccurate and unable to detect early-stage spoilage. Moreover, the absence of real-time monitoring and alert systems makes it difficult for users to take timely action, especially in households and food storage environments. To address these challenges, this project introduces a Smart Food Freshness Detection System using IoT and Machine Learning techniques. The system utilizes sensors such as gas, temperature, and humidity sensors to continuously monitor environmental conditions affecting food quality. The collected data is analyzed using Machine Learning algorithms to accurately determine whether the food is fresh or spoiled based on detected patterns. In addition to intelligent prediction, the system incorporates a real-time alert mechanism using GSM communication. When abnormal conditions or spoilage indicators are detected, instant notifications are sent to users, enabling quick response and preventing food loss. This proactive approach enhances monitoring efficiency and reduces dependency on manual checking methods.Furthermore, the system ensures continuous and automated operation by integrating sensor data collection, processing, and alert generation into a single framework. By combining IoT-based monitoring, Machine Learning-based prediction, and GSM-based notification, the proposed system provides a reliable and efficient solution for improving food safety, minimizing wastage, and supporting smart monitoring applications.Ensuring food freshness is essential for maintaining health and reducing unnecessary food wastage. In many cases, food spoilage goes unnoticed due to the lack of proper monitoring systems and reliance on traditional manual methods. These methods are not only time-consuming but also fail to provide accurate and timely information about the condition of food. To overcome these limitations, this project proposes a Smart Food Freshness Detection System that integrates IoT and Machine Learning for efficient monitoring and analysis