Published Paper Details:

Artificial intelligence together with deep learning technology has enabled the development of intelligent systems which reduce complexities in different daily life processes. The process of identifying suitable recipes for existing ingredients remains difficult for most people within the culinary domain. Users can benefit from this solution named "DishGenie - Recipe Recommendation System using Deep Learning" because it generates recipe recommendations from their provided ingredients. Having Convolutional Neural Networks (CNNs) and Natural Language Processing (NLP) as deep learning models allows the system to process textual data and recognize ingredients through images. Flask enables a user-friendly interface which allows people to enter ingredients to receive customized recipe recommendations and handle their personal recipe lists. The system combines structured database storage which verifies ingredients and maintains recipes alongside its operational functions. DishGenie implements artificial intelligence image identification with data-based recipe structuring coupled to an adaptive recommendation system that transforms eating experiences while reducing culinary waste through stimulated kitchen innovation. This document offers complete details about project development alongside its goals and actual influence on outcomes.The software system allows customization based on individual user food needs and recipes because it automatically adjusts to specific dietary rules and cooking abilities. The recommendation engine uses intelligence to evaluate ingredients for creating multiple recipes including standard daily meals together with sophisticated restaurant-style dishes. Supplementing features through external API integration allows users to access YouTube recipe videos along with nutritional details and substitute ingredients on the platform. The features at DishGenie enable users to select meals with knowledge while saving food and trying innovative cooking approaches.