<p>Visual place recognition is vital in enhancing destination marketing by helping service providers present offerings more effectively and enabling consumers to form stronger connections with locations. Integrating artificial intelligence into this process allows for smarter and more targeted marketing strategies through visual data analysis, content recommendations, and user behavior tracking. However, there are many challenges in visual place recognition due to the numerous image samples for processing, complex visual structures, and noise from non-recognizable images. This study explores the use of zero-shot learning (ZSL) for visual place recognition to address these challenges, particularly the issue of limited sample availability. We compared ZSL with traditional learning methods and evaluated multiple configurations, including city-, country-, and continent-level classification, as well as generalized zero-shot learning and label-based classification. Feature comparisons using 3D color histograms and gist descriptors were also conducted at test time. Our findings indicate that ZSL can effectively handle visual place recognition tasks, especially when proper class splits and configurations are applied. While the generalized ZSL approach, which includes seen classes during testing, often achieves higher success rates depending on the target set size, some ZSL configurations show performance comparable to traditional methods. These results highlight ZSL’s potential in real-world applications of visual place recognition and destination marketing.</p>