  In this paper, we present a novel approach for the design of leaky-wave
holographic antennas that generates OAM-carrying electromagnetic waves by
combining Flat Optics (FO) and machine learning (ML) techniques. To improve the
performance of our system, we use a machine learning technique to discover a
mathematical function that can effectively control the entire radiation
pattern, i.e., decrease the side lobe level (SLL) while simultaneously
increasing the central null depth of the radiation pattern. Precise tuning of
the parameters of the impedance equation based on holographic theory is
necessary to achieve optimal results in a variety of scenarios. In this
research, we applied machine learning to determine the approximate values of
the parameters. We can determine the optimal values for each parameter,
resulting in the desired radiation pattern, using a total of 77,000 generated
datasets. Furthermore, the use of ML not only saves time, but also yields more
precise and accurate results than manual parameter tuning and conventional
optimization methods.
