As a result of the rapid development of Internet technology, every individual now has their own mobile devices and computers, which allows them to get information on tourist destinations. When it comes to suggesting a selection for their vacation trip, the Tourist Recommended system plays an important part. When a person visits a certain area, they will provide feedback for each visit; this will have an effect on the decisions that new users make. Every single algorithm that is now in existence, such as collaboration or content filtering algorithms, makes use of the most recent user experience data in order to recommend the most suitable hotel. If the present user does not have any data from their previous experiences, then these algorithms will not operate. Utilizing the feature selection method in conjunction with the C4.5 decision tree technique allows us to circumvent the difficulties described above. The recommendation system that is being suggested is being built in order to provide suggestions for all other sites that are worth visiting.This Tourist Recommended System will be of more assistance in recommending destinations to visitors who are looking for places that are unknown to them. It does this by taking into account two elements, such as ratings and points of interest, and then determining the optimum location based on the values of each associated feature. [8] was the year when C4.5, an extension of ID3, was developed. Due to the fact that C4.5 attempted to address the primary issues of ID3, it was selected for this investigation. [9] ID3 The C4.5 method that Quinlan developed before may be used for classification purposes. This particular decision tree, known as C4.5 Decision Tree, is classified as Supervised Learning. The values of the characteristics are used as input by the Decision Tree, which then proceeds to provide the projected location based on the features that have been chosen