Recommender structures are developed to predict a client's preferences and recommend items that are likely to be relevant to them. They are most likely the most complicated AI computations used by web businesses to assist with transactions. Collaborative filtering, for example, finds a group of users based on the goods they buy or provide comments on and then recommends popular items in the group. Using variables like Video ID, hate, likes, favourite count, description, and keyword, a video recommendation engine is employed on the YouTube dataset. The most popular online video community on the planet is YouTube. Users' likes and dislikes on the site are used to suggest groups of videos to them. Collaborative content filtering algorithms were used in the suggested system. Data can be collected in a variety of ways, such as downloading with certain categories, ensuring that the information is always up to date. Users will see the top five YouTube videos based on the experimental results.