Public transportation wait times are a crucial factor influencing users' perception of the system's efficiency, their satisfaction, and their willingness to continue using these services. This study analyzes long wait times in public transportation and identifies the most affected users in the Metropolitan Area of Valpara & iacute;so, Chile. Using data from the Gran Valpara & iacute;so Mobility and Transportation Survey conducted by the Transportation Planning Secretariat (SECTRA) in 2014, only public transportation trips were selected, resulting in a dataset of 17,951 records. Exploratory data analysis techniques and Artificial Intelligence algorithms, such as DBSCAN clustering, were applied, as well as Moran's Index for spatial autocorrelation, in order to identify patterns and groups of users experiencing prolonged wait times. The results show that certain demographic groups and specific geographic areas face longer wait times, negatively impacting equity and accessibility within the public transportation system. This study provides insights for improving transportation planning by identifying patterns and user groups that experience extended wait times, which can guide decisions to enhance user satisfaction and promote the use of public transportation.