Soft matter systems, such as foams and emulsions, play a central role in numerous applications for consumer goods, mineral beneficiation, and enhanced oil recovery. Detailed microstructural information of the soft matter system enables accurate modeling of its aging mechanisms and bulk mechanical properties. Foams and emulsions are traditionally characterized in 2D using microscopy or evaluating bulk properties, which lack the full 3D vision of the internal structure of the system. Herein, X-ray computed microtomography (µ-CT) coupled with advanced image processing tools is used for extracting detailed microstructural information of foams and emulsions. The derived morphological information is used for evaluating the aging mechanisms of liquid foams, and the arrested coalescence of oil-in-water emulsions. Firstly, a workflow for 3D µ-CT imaging and pore network modeling (PNM) is developed to characterize drainage, coalescence, and diffusive-coarsening in liquid foams. PNM is useful for decomposition of the Plateau borders and nodes within the liquid structure of the foams, while µ-CT provides time-lapsed spatial information. Foam permeability (ĸ) simulations conducted on the extracted PNM are shown to aid foam drainage modeling by the extraction of ĸ versus liquid fraction relationships. Secondly, challenges in using laboratory µ-CT systems are dealt with by the implementation of deep learning. It is demonstrated that deep learning can reduce scan times to only 5 minutes, thus, allowing high temporal resolution for the study of fast-aging liquid foams. These developments are achieved using a laboratory µ-CT system that is traditionally used for imaging static systems over hour-long scan time. Thirdly, novel measurements for droplet networks of oil-in-water emulsions formed via arrested coalescence are presented. Combination of topological and geometrical measurements is demonstrated as effective means for evaluating the stabilizing forces present in the arrested system. Particularly, linear strain measurements of the 3D droplet network elucidate the distribution of strain and stresses within the network, which is not possible to observe in 2D studies. Overall, the dissertation is a step forward in advancing 3D µ-CT imaging for characterizing and modeling soft matter systems.