In order to maintain high standards of both food safety and quality in the event of a pandemic, the authors of this research suggest a model that is underpinned by both the Internet of Things and machine learning. The proposed system makes use of a wide variety of sensors and Internet of Things (IoT) devices in order to keep track of the whole food supply chain, from the farm to the consumer's plate. These sensors gather a variety of data elements, including temperature, humidity, and other environmental conditions that may have an impact on the quality and safety of the food. The gathered information is then input into a Convolutional Long Short-Term Memory (Convolutional - LSTM) machine learning techniques, which employs a number of algorithms to conduct an analysis of the information and locate any possible threats to food safety. The model is also capable of predicting the possibility of food contamination and spoiling depending on a variety of criteria including temperature, amount of time, and storage conditions. During a pandemic, the suggested system may assist authorities in charge of food safety to more rapidly recognize and react to any threats to food safety, hence protecting the integrity of the food supply chain and maintaining its high standard of quality. In addition to this, it may assist food organizations in enhancing their quality control procedures, decreasing the amount of waste they produce, and preserving the contentment of their customers.