It is well established that city life can impact on individuals’ mental well-being. Factors associated with modes of transport in a city, such as cycle corridors and the reliability of bus network, and environment factors, such as availability of green spaces, have been shown to relate to individuals’ well-being in the city. Smart cities contain a wealth of digital data which has been used in the management and organisation of cities. Such data is gathered from sensors, networks and systems which contain rich insights on factors associated with city life. Such as, for example, the availability of open spaces in the city, traffic congestion, and air quality levels. We propose that these smart city data sources and data flows can act as contextual cues to indicate the mental well-being of individuals in the city. That is, we propose harnessing indicators and patterns in datasets known to be associated with well-being, and using these as contextual cues for automated city well-being level estimation. In this initial investigation, we focus on contextual cues associated with active travel and transportation, environmental information and green infrastructure. We propose an AI-based system which uses these contextual cues to generate an indicator of mental well-being in the city.