The rapid expansion of tourism across the world necessitates constant innovation and development in the services offered to visitors in order to assure their comfort and happiness while on the road. Travelers’ experiences may be greatly enhanced by providing them with basic and essential conveniences such as optimal route identification and suggestion technology. In this paper, we use data mining to investigate the effect of scenic site clustering and group emotion on tourist route choosing. It is common for traditional route selection algorithms to just examine the impact of picturesque locations on route design. Many people choose the Chimp optimization algorithm (ChOA) because of its straightforward idea, simple implementation, and high level of resilience. With the goal of solving practical challenges in mind, this study uses real-world geographic data to build a discrete ChOA for the tourism route planning problem, which may be applied in practice. Simulation experiments are done, and outcomes data are studied and assessed. The assessment findings show that the ChOA is suitable for mass tourist data mining. The smart machine’s final best tour routes are directly tied to the requirements, interests, and habits of visitors and are completely connected with geospatial services to ensure accuracy. The ChOA algorithm serves as a good example of how data mining may be used in the field of mass tourism.