Many of the most prevalent and challenging human diseases—including cancer, cardiovascular disease, and fibrotic disease—emerge from dynamic, multiscale interactions among heterogeneous cell populations and their evolving microenvironments. Yet most therapeutic strategies and computational models focus on isolated molecular targets, limiting their ability to predict or influence emergent tissue-level behavior or patient-specific outcomes. Addressing this gap requires mechanistic models that explicitly link intracellular signaling, cell phenotypic decision-making, and tissue-scale remodeling across space and time. In this talk, I will present our laboratory’s work developing multiscale mechanistic computational models that integrate molecular, cellular, and tissue-level processes to study disease onset and progression. Our approach centers on agent-based modeling, enabling explicit representation of individual cells, stochastic behaviors, and cell–cell interactions while capturing emergent, tissue-scale dynamics. These models are specified and parameterized using data-driven, machine learning approaches, allowing us to incorporate high-dimensional experimental data while preserving mechanistic interpretability. Importantly, our modeling efforts are tightly coupled to experiments in an iterative loop. We design experiments to probe single-cell behaviors and microenvironmental cues, use these data to refine model structure and parameters, and then generate testable predictions regarding tissue-scale adaptations. Using this framework, we have studied microvascular network and tissue remodeling in development, regeneration following injury, and fibrotic disease. Together, these studies demonstrate how multiscale mechanistic models can reveal causal links between cellular decision-making and tissue-level disease phenotypes, providing a predictive foundation for rational therapeutic design.