In today’s competitive edge of the business world, there is a need for a systematic decision-making process in every field. The tourism field is one of the fast-growing filed in the current scenario which comprises a huge amount of information in an unstructured format which causes information overload. This field has diverse information and huge exposure at the national and international levels. To extract meaningful information and pass it on to the appropriate customer is a real challenge. Tourists in a current digitized scenario did not depend and trust on tourist place advertisement, travel guide, etc. rather they did their analysis by using the search engine. Getting correct traveling information using a traditional search engine is a quite tedious task. A recommendation system is the best information filtering system to get relevant and meaningful information based on an individual's needs with the help of artificial intelligence. A recommendation system focusing on the tourism domain will capture consumer travel interests and suggests a suitable place to visit. User’s travel history is the prime factor considered to recommend places that suits them. The source for knowledge acquisition on the tourism domain was gathered primarily from various sources like by surveying multiple users and through web scraping. We introduce various models and methods to provide recommendations to tourists. Our system also generates a dynamic number of recommendations to provide based on user selection. We propose a model based on a collaborative filtering approach that provides a worthy recommendation in the field of tourist field. In the existing system, people who want to travel have to search a lot of tourism websites. Also due to a lot of information available on the internet they might get confused. This problem is resolved by modules which recommend tourist places to people according to their interest. That is basically, it uses machine learning algorithms.