This study investigates the untapped tourism potential in Maharashtra state, many attractions remain confined to local proximity, failing to reach a broader audience. The allocation of government or private sector funding is contingent upon a site’s perceived tourism potential, necessitating destinations to demonstrate their capacity to attract visitors. To address these issues, we propose an innovative solution centered on providing real-time suggestions. To address these challenges, we propose a machine learning-based web application designed to promote lesser-known sites, thus enhancing local economies, preserving heritage, and offering personalized experiences to tourists. This application utilizes a Random Forest model to classify locations based on popularity, filtering recommendations based on user proximity using the Haversine formula. The methodology includes user location through GPS providing dynamic and relevant suggestions based on geographic data. The system has never been implemented before, positioning it as a novel approach in the field of tourism technology.