The need of safe and livable indoor environments has intensified recently, given the great amount of time people spend indoor. In addition, the recent COVID-19 pandemic has moved the interest from the outdoor to indoor spaces. To guarantee an optimal indoor environmental quality, monitoring and regulating many variables (such as indoor and outdoor temperature, pollutants concentration, noise, and brightness) is necessary. In this context, we have developed AIR SAFE, an IoT and AI based infrastructure to monitor and control environmental quality in closed spaces. AIR SAFE uses Machine Learning models to make predictions of temperature, relative humidity, and CO \(_{2}\) concentration. These predictions, together with data from a network of IoT sensors, are used to take actions on windows and the air conditioning system with the aim of modifying for the better the room environment. We show the results of the AI model we have developed for predicting indoor concentration of CO \(_{2}\) , relative humidity, and temperature. Our Long Short Term Memory (LSTM) model has been tested against literature models using simulated data at first, and then testing the best models on real data. Using real data, the LSTM network performs best at forecasting temperature and relative humidity, while Random Forest is the best CO \(_{2}\) concentration predictor.