The glymphatic system facilitates the removal of waste products from the brain via an extensive network of perivascular pathways that enables the dynamic exchange of solutes between cerebrospinal fluid (CSF) and interstitial fluid (ISF). Dysfunction in this system can lead to the deposition of aberrant proteins such as amyloid beta (Aβ) and tau agglomerates, which are key biomarkers for Alzheimer’s disease and other neurodegenerative conditions. Therefore, capturing and analyzing the underlying mechanisms of glymphatic transport and deposition of these biomarkers is crucial for identifying strategies to prevent or slow the progression of neurodegenerative diseases. However, the glymphatic system remains poorly understood due to its inherent complexity, the multi-scale nature of its processes, and the limitations of existing models, which often rely on oversimplified geometries and material properties. In this work, we aim to address these challenges by developing an image-guided computational framework to gain a quantitative understanding of glymphatic transport and deposition of molecules and proteins throughout the brain, using immersed isogeometric analysis stabilized by the streamline upwind Petrov–Galerkin (SUPG) method. To simulate Aβ transport and deposition, we employ an unsteady advection–diffusion equation coupled with an irreversible amyloid plaque deposition model. The diffusive field is derived from diffusion-weighted magnetic resonance imaging (DW-MRI) data of a mouse brain, while the subject-specific brain geometry is extracted from the same imaging dataset. The advective field is reconstructed through solving the inverse problem of velocity estimation, informed by contrast-enhanced MRI (CE-MRI) data acquired following the injection of a tracer into the CSF via the cisterna magna, with imaging performed over a two-hour period at discrete time points to capture the tracer dynamics. Finally, plaque deposition model parameters are calibrated against CE-MRI-based estimation of amyloid plaque burden in 2-month- and 7-month-old subjects through an inverse problem approach. The model predictions are then validated against plaque deposition data obtained from 12-month-old subjects, demonstrating the framework's feasibility and predictive capability. By integrating image processing, computational simulations, parameter tuning, and model validation, the framework offers a robust pipeline for predicting glymphatic transport and Aβ deposition.