Urban air pollution remains one of the most critical environmental health challenges in large metropolitan areas, particularly in Latin America. Traditional monitoring systems, while reliable, are often limited in spatial and temporal resolution, leaving gaps in the detection of local pollution events. This study presents a data-driven approach to analyze citizen complaints related to air pollution in Mexico City by leveraging unstructured data from Twitter. A semi-automated pipeline was developed for the extraction, cleaning, transformation, and analysis of over 500,000 tweets collected between 2019 and 2022. After preprocessing, 69,739 tweets were subjected to topic modeling using Latent Dirichlet Allocation (LDA), enabling the identification of twelve semantic categories associated with pollution events—such as traffic congestion, fire outbreaks, fireworks usage, and emergency response. Spatiotemporal analysis revealed that the boroughs of Miguel Hidalgo, Cuauhtémoc, and Iztapalapa recorded the highest number of reports, particularly during festive periods and dry seasons in Mexico City. The results highlight the strong potential of social networks as complementary sources of environmental information, capable of capturing citizen perception, behavior, and real-time local incidents often excluded from institutional air quality datasets. A Power BI Geo-dashboard was developed to facilitate interactive visualization and exploration of pollution-related complaints, supporting evidence-based urban decision-making. This study contributes a replicable framework for social media–based environmental monitoring and advocates for the integration of citizen-generated data into urban air quality management strategies.