This work presents the development of a personalised intelligent tourist route recommendation system based on artificial intelligence and soft computing methodologies. The system is made up of different modules and each one includes intelligent techniques and tools, leverages customer data, information on points of touristic interest, and user feedback to offer personalised recommendations. Specifically, by applying clustering techniques, customers are segmented into similar groups, allowing for more precise targeting. Customer segmentation facilitates profile analysis to capture the characteristics and preferences of tourists. Using content-based and filtering recommendation methods, the system further personalises POI selections. Moreover, the integration of metaheuristic algorithms, such as GRASP, addresses challenges in route planning and takes into account user restrictions such as location, preferred attractions, and available time. Additionally, the integration of visualisers and mobile interfaces represents an important impact on the success of recommendation systems on platforms to enhance user satisfaction and the efficacy of recommendations. This comprehensive approach not only improves the user experience, but also contributes to the success and adoption of recommendation systems in the tourism area.