Traditional antenna optimization solves the modified version of the original antenna design for each iteration. Thus, the total time required to optimize a given antenna design is highly dependent on the convergence criteria of the selected algorithm and the time taken for each iteration. The use of machine learning enables the antenna designer to generate trained mathematical model that replicates the original antenna design and then apply optimization on the trained model. Use of trained model allows to run thousands of optimization iterations in a span of a few seconds.