This study examines how the analysis of tourist photographs on Instagram, using web scraping and artificial intelligence techniques, can enhance sustainable tourism management. By analyzing nearly 150,000 posts from a specific destination, the research identifies key patterns in social media engagement (SME) related to the number of people in the photos, and the type of photos. The findings reveal that photos featuring more individuals garner higher SME, enabling destination managers to better predict tourist preferences and manage visitor flows to prevent overcrowding. The study also differentiates between the effects of selfies and panoramic photos on SME, suggesting that while selfies generate more comments, panoramic photos may have stronger visual appeal. These insights are essential for developing targeted marketing campaigns that mitigate overcrowding and support sustainable tourism practices. This research makes a significant contribution to the fields of tourism marketing and sustainable development.