This thesis presents a contribution to the evaluation and modeling of high-speed electrical interconnects on printed circuit boards up to 100 GHz using machine learning. Parametric studies of different models are conducted with efficient numerical methods. The crosstalk is analyzed for different interconnect types. Machine learning methods are introduced as an alternative design method. They are evaluated regarding the reliability of the predictions and the necessary effort.