The primary goal of the suggested methodology is to create a sophisticated recommendation system that uses Convolutional Neural Networks (CNN) with YOLOv5 for image classification and detection. To achieve this, the recommendation system processes user-uploaded images and uses CNN with YOLOv5 architecture to extract features in order to accurately identify tourist destinations. The LLM component then assesses when it is appropriate to visit the locations that have been discovered, taking into account a number of variables like the weather and the seasonality of tourists. Our system’s scheduling function comprises the creation of a set of tourist destinations and an assessment of their suitability for tourism. The system iteratively modifies suggestions in response to user preferences and current conditions, with the goal of minimizing time spent at each location while optimizing the overall quality of the traveler experience.