Advanced IoT-Embedded Culinary Intelligence Using RNN Model for Smart Kitchen Solutions with Food Waste Reduction Features

This paper introduces a new system that integrates IoT with Recurrent Neural Networks (RNNs) to improve kitchen management. The system employs Internet of Things (IoT)-enabled kitchen equipment to track freshness, expiry dates, and ingredient availability in real-time. RNNs can help us make informed decisions and reduce food waste by providing individualized recipe ideas based on available components. Data collecting, modeling, and decision-making compose the framework. Kitchen appliance IoT sensors monitor food freshness, expiry dates, and ingredient stockpiles. The labeled dataset-trained RNN model predicts optimum resource use and generates customized recipe recommendations using the data. Considering customer preferences and dietary constraints, these ideas improve the culinary experience. Combating food waste promotes Sustainability and environmental responsibility. By encouraging conscious eating, the system helps create sustainable food systems worldwide. Furthermore, the framework promotes resource efficiency and ethical use. The framework bridges technology and Sustainability in smart kitchen products. Through IoT sensors and RNN-driven analytics, the system allows users to cook more efficiently and sustainably, creating a greener kitchen.