People are very indecisive on choosing what to eat. Many individuals spend more time contemplating what to eat rather than actually eating. Therefore, a web-based food recommendation system is developed to help to resolve this problem. The purpose of this report is to outline the process of developing a web-based food recommendation system. The web-based food recommendation system offers people with food recommendation based on their mood, food category preferences, and budget. The frameworks BALC and SDLC were utilised to construct the system. Data used in this project was extracted from Kaggle and another dataset were extracted from a journal to support our predictive model. Using Python, data preparation is performed in Jupyter Notebook. In addition to Python, NLTK (Natural Language Toolkit) is used to develop the predictive model. Predictive model has been built to define the top three foods that people for a mood, and it has been incorporated into the website. Data visualisation technologies such as Tableau are used to create data visualisations that aid in the comprehension of datasets. The development for the website is built using PHP and CSS. MySQL is also used to stores the user’s login ingo. By developing a system that will provide food recommendation based on user’s mood, food category preferences and budget will help people to save time and reduce the decision-making time.