The development of smart and healthy cities requires the creation of urban frameworks that comprehensively assess safety levels in transportation systems. Mexico City’s subway system (Metro) is the eighth most traveled in the world, while the city’s Bus Rapid Transit system (Metrobús) transports approximately 1.8 million passengers daily. However, the Metro has been rated as one of the least secure electric transport systems, underscoring the need for effective strategies to mitigate safety risks. This study proposes a framework based on artificial intelligence and open data to assess security at Metro stations and Metrobús stops. Crime records from 2019 to 2022, georeferenced transport network layers, and passenger flow data were analyzed to identify crime patterns within public transportation environments. Findings reveal that women are the primary victims of harassment and sexual abuse, whereas men are more frequently targeted for street robberies and passenger thefts inside Metro trains. Additionally, crime rates peak at noon, with the most vulnerable age group being individuals aged 21 to 30. Notably, half of the recorded Metro-related crimes occur within the influence zones of Lines 1, 2, and 3. Based on these insights, a predictive model using SARIMA and Support Vector Machines (SVM) was developed to estimate weekly crime risk at transport stations and their surrounding areas. The SVM model achieved 70% accuracy, offering a valuable tool for anticipating high-risk locations and times, which could inform the development of more effective security strategies for public transportation.