Metropolitan areas around the world are experiencing a surge in air pollution levels due to different anthropogenic causes, making accurate air quality prediction a critical task for public health. Although many prediction systems have been researched and modelled, many of them have neglected the different effects that air pollution has on each individual citizen. Hence, we present a novel context prediction model that includes context-aware computing concepts to merge an accurate air pollution prediction algorithm (using Long Short-Term Memory Deep Neural Network) with information from both surrounding pollution sources (e.g., bushfire incidents, traffic volumes) and user's health profile. This model is then integrated into a tool called My Air Quality Index (MyAQI), which is further implemented and evaluated in a real-life use case set up in Melbourne Urban Area (Victoria, Australia). Results obtained with MyAQI show both that (i) high precision levels are reached (90–96%) when forecasting air quality situations in four air quality monitoring stations, and (ii) the proposed model is highly adaptable to users' individual health condition effects under the same airborne pollutant levels.