Data-driven approaches have proven to be very efficient in many vision tasks, and they are now used for optical parameters’ optimization for application-specific camera designs. Methods such as neural networks are used to estimate camera performance indicators related to the point spread function—such as the root mean square (RMS) spot size—from optical parameters. Such procedures help to understand the connection between optical characteristics and push optical design expertize beyond its limits. We investigate these approaches to model the interaction between the distortion of wide-angle designs and their RMS spot size, which is not explained by aberration theory. Specifically, we test off-the-shelf data-driven methods to determine in which conditions we can establish a model that is able to predict the variations of the RMS spot size along the field of view from the distortion function even in the absence of a mathematical model. Although current methods focus on building accurate models often usable for very specific designs—composed of a few elements only, we present a methodology focusing on more complex and realistic wide-angle designs.