Scanning Transmission Electron Microscopy and Piezoresponse Force Microscopy has opened a window into atomic and mesoscale functionalities of ferroelectric materials, providing a wealth of information on atomic coordinates and order parameter fields, their behavior at surfaces, interfaces, and topological defects, and polarization dynamics. However, this wealth of data necessitates development of pathways to extract the generative physics of ferroelectric materials, either in the form of parameters of mesoscopic Ginzburg-Landau model, or corresponding atomistic descriptors. In this presentation, I will discuss the several examples of machine learning based exploration of ferroelectric materials on the atomic and mesoscale level. One such approach is based on the Bayesian methods that allow to take into consideration the prior knowledge the system and evaluate the changes in understanding of the behaviors given new experimental data. The second pathway explores the parsimony of physical laws and aims to extract these from the set of real-world observations. Finally, the Bayesian networks can be used to explore the causative relationships in the multimodal data sets. Ultimately, we seek to answer the questions such as whether frozen atomic disorder drives the emergence of the local structural distortions or polarization field instability drives cation and oxygen vacancy segregation, whether the nucleation spot of phase transition can be predicted based on observations before the transition, and what is the driving forces controlling the emergence of unique functionalities of morphotropic materials and ferroelectric relaxors.