Real time monitoring, forecasting and modeling air pollutants’ concentrations in major urban centers is one of the top priorities of all local and national authorities globally. This paper studies and analyzes the parameters related to the problem, aiming in the design and development of an effective machine learning model and its corresponding system, capable of forecasting dangerous levels of ozone (O3) concentrations in the city center of Athens and more specifically in the “Athinas” air quality monitoring station. This is a multi parametric case, so an effort has been made to combine a vast number of data vectors from several operational nearby measurements’ stations. The final result was the design and construction of a group of artificial neural networks capable of estimating O3 concentrations in real time mode and also having the capacity of forecasting the same values for future time intervals of 1, 2, 3 and 6 h, respectively.