In today’s digital world, tourism has undergone a significant change with the introduction of advanced technologies. Applications have become essential in making travel experiences better through real-time information, personalized recommendations, and easy navigation. In today’s fast world, people want efficient and personalized ways to explore places. Traditional travel guides and generic recommendation systems fail to cater to individual tastes. This paper presents a proximity-based tourism recommendation system integrating text summarization and multilingual support. The system utilizes BERT for extractive summarization, a content-based filtering approach for recommendations, and the Haversine formula for distance computation. Implemented as a Flask-based web application, the system provides personalized travel guidance, improving accessibility and efficiency of trip planning. Experimental results demonstrate enhanced user engagement and satisfaction compared to conventional travel recommendation approaches.