Food is one of the necessities of life. The food we consume every day provides us with the nutrition we need to have energy. However, food plays a more significant role in life. There is a relationship between food, culture, family, and society [1]. Since ancient civilization, people have realized the correlation between food and healthiness. Earlier, Physicians were treating people by prescribing special recipes. Last century, assorted studies investigated the impact meals have on human nutritional intake and the different diseases connected to it. There have been numerous other studies that focused on the required nutritional intake to ensure a good amount of energy for well-being in humans. A person who advises individuals on their food and nutrition is known as a dietarian and nutritionist. Nowadays nutritionists are experts in the use of food and nutrition to promote health and manage disease. They suggest several diet rules and food recommendations to assist people in living a healthy life. Due to technological advancements, previous time-consuming issues that required human attention are now being solved by utilizing automated procedures machines. Meal planning is one of the attractive domains that recently has received great notation by researchers who are using machine learning techniques in it. In general, those studies were performed to use extracted nutrition knowledge and food information for designing an automated meal planning system. However, in the majority of published research, the user’s preferences were an ignored feature. In this research, my journey through developing automated meal planning systems unfolds across distinct projects, each building upon the insights and advancements of its predecessors. Starting with a focus on incorporating user preferences, the exploration evolved through successive iterations, seeking to mirror the complexities of real-world decision-making more accurately. This progression led to the integration of advanced methodologies spanning artificial intelligence, optimization, multi-criteria decision making, and fuzzy logic. The ultimate aim was to refine and enhance the systems to not only align with users’ dietary restrictions and preferences but also to adapt to user feedback, thereby continually improving their efficacy and personalization. Through this comprehensive approach, the research endeavors to contribute novel solutions to the nuanced challenges of personalized meal planning.