This study aims to ascertain the implementation of an air quality monitoring system and artificial intelligence modeling for predicting air quality in a multi-use facility setting. We designed and deployed a system with sensors for temperature, humidity, TVOC, CO, CO₂, PM₁₀, and PM₂.₅ in a wireless real-time environment within a multi-use facility, collecting 4373 sensor measurements during a month-long trial. We conducted LSTM ensemble modeling and predictions for each of the seven sensor measurements and calculated the RMSE, MAE, MAPE, accuracy, and loss metrics to measure the model performance. The monthly average values of TVOC (1853.54±2859.28, Q1: 168, Q3: 2114), PM10 (9.42±6.89), and PM₂.₅ (8.93±6.01) were analyzed. Monitoring the air quality of a multi-use facility for a month revealed daily and weekly periodicity in TVOC, CO₂, PM₁₀, and PM₂.₅ through seasonal decomposition analysis. Predictions were performed using the LSTM ensemble network, and the MAPE of the temperature and humidity were analyzed as 0.04 and 0.06, respectively. This preliminary study confirmed that the developed system and AI model, which measure and predict air quality, can be deployed in hospitals and other multi-use facilities that cater to vulnerable groups.