Datacenters play an increasingly crucial role in supporting a great variety of services, from cloud computing and data storage, to streaming services and artificial intelligence. Their growing high computing resources utilization, along with all the cooling system mechanisms to manage the generated heat, have led to significant environmental impacts and CO2 emissions. To address these challenges, this thesis proposes a predictive optimization mechanism aimed at reducing energy consumption and consequently environmental impacts. The research begins with the development of a Random Forest regressor capable of estimating the average power consumption of each submitted job, based on its resources demand specified by the user.