One of the leading causes of early health detriment is the increasing levels of air pollution in major cities and eventually in indoor spaces. Monitoring the air quality effectively in closed spaces like educational institutes and hospitals can improve both the health and the life quality of the occupants. In this article, we propose an efficient indoor air quality (IAQ) monitoring and management system, which uses a combination of cutting-edge technologies to monitor and predict major air pollutants like CO2, PM2.5, TVOCs, and other factors like temperature and humidity. The aim is to create an intelligent environment for IAQ. The data is captured and monitored using an Internet of Things network of sensors, manufactured by ourselves, in different lecture rooms at the university. The obtained data is then processed and correlated in real time using a complex event processing engine and analyzed by machine/deep learning algorithms. A long short-term memory neural network is proposed to forecast IAQ. Then a decision tree regressor is used to identify the relationships between temperature, humidity and different pollutants like CO2 and PM2.5.