Nanomedicine has emerged as a promising approach for improving the delivery, targeting, and efficacy of therapeutics using engineered nanoparticles (NPs). Predicting NP behavior across biological scales is required and opens up valuable opportunities for computational approaches supporting improved clinical translation. This thesis develops a comprehensive multiscale modeling framework to study the key physiological, vascular, and immune processes that govern nanoparticle biodistribution, transport, and clearance. The framework centers around a physiologically based pharmacokinetic (PBPK) model that simulates systemic NP distribution and organ-specific accumulation by incorporating physiologically relevant transport kinetics and NP-specific parameters. To capture microvascular dynamics, the model is augmented with a margination module based on a DeepONet-coupled Fokker–Planck equation, which accounts for hematocrit-dependent radial drift and NP size effects on near-wall accumulation. At the immune interface, an agent-based model of the complement system identifies a percolation-type threshold for complement activation based on surface spacing of ligands, providing insight into immune recognition and clearance. Additionally, phagocytic interactions are modeled at the membrane scale to inform NP degradation rates in tissue compartments. A key feature of the framework is the coupling of biophysical insights from margination, immune activation, and degradation models back into the PBPK model to account for their effects on systemic distribution. This allows for physiologically consistent modeling of NP accumulation, clearance, and immune response. The resulting continuum framework is computationally efficient, enabling fast and modular simulation of mechanistically detailed scenarios. This supports a prediction-first approach to nanoparticle design, facilitating model-guided optimization of NP properties prior to in vivo experimentation.