Calibration of a numerical process model against laboratory or field data is often referred to as “inverse modeling.” As the numerical simulation models become more complex, the number of parameters to be estimated generally increases, requiring new testing, modeling, and inversion strategies. The purpose of this survey is to review inverse modeling approaches for unsaturated and multiphase flow models. The discussion focuses on applications rather than theoretical considerations, which have been previously reviewed in the context of saturated flow and transport modeling. We also examine model parameterization issues, specifically the representation of heterogeneity through a limited number of variables that can be subjected to parameter estimation and uncertainty propagation analyses. Different parameterization strategies are illustrated using the multiphase flow simulation–optimization code iTOUGH2. A comprehensive inverse modeling package (such as iTOUGH2, which includes automatic model calibration followed by an extensive residual, error, and uncertainty propagation analysis) is an essential tool to improve test design and data analysis of complex multiphase flow systems.