The rapid advancement of artificial intelligence has enabled the development of intelligent systems capable of analyzing food images and supporting informed dietary decisions. This study presents an integrated AI- based framework for visual food understanding, quality and safety assessment, and personalized nutrition recommendation. The primary objective is to address challenges in identifying food items, evaluating freshness, and providing accurate nutritional insights within a unified system. The proposed approach combines deep learning techniques, including convolutional neural networks for food classification and freshness detection, along with transformer-based models for recognizing fruits and vegetables. External nutrition data sources are integrated to retrieve detailed information such as calorie content and macronutrient composition. A web-based interface enables real-time interaction, allowing users to upload images and receive predictions. Experimental results demonstrate reliable performance with high prediction accuracy. The system contributes by offering a scalable solution for health monitoring, dietary planning, and food safety awareness.

Keywords— Artificial Intelligence; Food Classification; Freshness Detection; Deep Learning; Nutrition Analysis; Personalized Diet Recommendation