The geometrical and material properties of distributed electromagnetic structures comprise the design space. This space characterizes the structure’s frequency response in complex domain. In this paper, we propose a machine learning framework for predicting frequency response of a power delivery network as a function of its extrapolated multidimensional geometrical and material parameters. The proposed approach comprises of an ensemble of architectures: (1) Fully Connected Upsampler for latent code generation (2) Convolutional Decoder to learn the frequency response from the latent code. The 14D design space is converted to a Lth dimensional code which entails the frequency response information. With the proposed architecture, a root mean squared error of 0.004 ohms is achieved when compared to the true value. We focus on extrapolation of design space parameters while training on in-band values. We also illustrate how frequency poles move with varying design space exploiting parameter sensitivity in different frequency bands.