One of the leading problems around the world is unhealthy eating habits and a lack of awareness of what to eat based on the body conditions which further leads to the development of chronic diseases. For instance, if you visit a food store. Most of the time people are aware of their blood levels and sugar but unaware of the content of food that can affect these levels. So here we try an automated recommendation system with the help of machine learning techniques to recommend various foods based on their contents with user input such as height, weight, etc. and even to get a personalized diet plan based on this value. Many users from underdeveloped regions don’t have access to personal nutritionists or may be unable to afford such a facility. The key advantage of such a system is that it can take into consideration an individual’s dietary restrictions and preferences and provide a tailor-made service for that individual. We employed K-Means clustering on preprocessed recipe data after applying outlier detection, scaling, and normalization. Principal Component Analysis (PCA) was utilized for dimensionality reduction to visualize high-dimensional data in a lower-dimensional space. The clustered data was visualized using scatter plots, providing insights into recipe groupings based on nutritional content. Evaluation metrics including Silhouette Coefficient and Davies-Bouldin Index were calculated to assess the quality of the clusters Further, for the interaction of users a web-based front end will be developed for user input and recommendation output.