The integration of machine learning with mechanistic modeling is transforming the field of mathematical oncology. This lecture introduces Localized Convolutional Function Regression (LCFR), a novel AI-driven framework for analyzing dynamic contrast-enhanced MRI (DCE-MRI) data to noninvasively quantify interstitial fluid transport in tumors. LCFR leverages weak-form regression and domain-specific basis functions to estimate spatially varying coefficients of partial differential equations governing advection-diffusion-reaction dynamics. This approach enables simultaneous measurement of perfusion, diffusion, and interstitial fluid velocity in 3D, overcoming limitations of traditional voxel-wise ODE fitting and enhancing interpretability and computational efficiency. Key topics will include: The mathematical formulation of LCFR and its connection to sparse identification of nonlinear dynamics (SINDy). Validation across in silico, in vitro, and in vivo models, including hydrogel phantoms and murine glioma. Application to clinical imaging data from glioblastoma and breast cancer patients, revealing tissue-specific differences in fluid dynamics. Implications for understanding tumor microenvironment, drug delivery, and treatment response. This lecture will provide attendees with a conceptual and practical foundation for integrating AI-based model discovery into clinical imaging workflows, offering new avenues for personalized cancer modeling and predictive analytics in oncology.