Today, if you are suffering from a chronic disease, you need not only good medicine, but also a proper diet. A recommender system only predicts which products a user can purchase. Traditionally, there have been two types of recommendation techniques. Content-based collaborative filtering techniques are used accordingly. This type of filtering technique is called hybrid filtering. Using the techniques described above, we use your personal information such as your age, height, weight, diet type, nutrients, and related medical conditions. It finds similarities between users and uses the KNN algorithm to recommend types of foods to include in your diet. There are basically two types of data sets that are mainly used to get a better knowledge of the data. The first is about user profiles that contain personal information, and the second is about specifically recommended food types and ingredients gleaned from various web scraping sites. There are plenty of vegetarian and non- vegetarian styles, so you can choose your favorite from the recommended menu. This recommendation process can be used to personalize content such as groceries to users, specifically telling them what to eat based on their interests. If groceries aren't available, you can also order groceries whose purchase history is also saved in your profile. This way, you can cook for yourself without going to a specific nutritionist, saving you money and time. It has to do with building a dating system.