Recently the use of Artificial Intelligence (AI) in the tourism sector has increased rapidly, innovating the way travelers organize their trips. The generation of personalized itineraries is an area with great advances, offering personalized and optimized travel plans to fulfill individual preferences. This research presents a prototype of a mobile application designed to provide personalized travel itineraries for tourists in Mexico. The study employs a mixed-methods approach, combining quantitative data analysis with qualitative interpretation to gain insights into tourist sites in Mexico City and the personal preferences of the traveler. The study aims to provide the architecture of the app. The app aims to create an itinerary based on the user preferences and interests, geographical information and tourist attractions by employing machine learning techniques and a vast dataset of points of interest or attractions in Mexico City. Still in its early stages, the goal is to enhance the tourist experience by offering tailored recommendations and improve the planning process experience since currently travelers often encounter an overwhelming amount of information, limited time to explore or plan and little local knowledge to have a complete and good experience The prototype focuses on the development of a user-friendly interface and the integration of various data sources to create dynamic and engaging itineraries.