Innovations in optical networks created new technological challenges as routing and spectrum allocation (RSA) problem, fragmented spectrum, the need for rapid and efficient channel restoration, and put pressure on the operation and maintenance. With a lot of lots of variables or knobs to adjust, it is crucial to improve the automation as traditional algorithms are not able to handle the network efficiently. So, this requires sensors, network abstraction, actuators and SDN (Software Defined Networking) in order to run algorithms on top, control, manage the network and make decisions. In addition to this, there are the requirements for low-margin systems and probabilistic shaping. So, machine learning (ML) provides a collection of techniques to adapt to this dynamic and flexible environment and provide a network that learn from experience, optimize and make the networks agile, robust, dynamic, and smarter. At the same time, network automation together with machine learning may cause an explosion in power consumption, making this solution costly, inefficient and not sustainable.