Alzheimer’s disease (AD) is one of the leading causes of death in the United States. It is a neurodegenerative disorder that affects cognitive abilities, characterized by deterioration of the brain tissue due to synaptic loss caused by the abnormal accumulation of amyloid-β (Aβ) peptide and hyperphosphorylated tau proteins, leading to the formation of senile plaques and neurofibrillary tangles, respectively. Mathematical models based on AD biomarkers can be used as a tool to estimate disease progression kinetics, make disease prognosis, determine novel treatment strategies, and develop patient-specific treatment regimens. In this thesis, I developed a novel kinetic model, formulated as a system of differential equations, and a dynamic network diffusion model to characterize the spatiotemporal evolution of pathological biomarkers during AD progression. The kinetic model was calibrated with the ADNI database and simulated the temporal evolution of the five variables of the model: CSF tau, CSF phosphorylated tau, neuronal activity, CSF soluble Aβ, and Aβ plaques. Additionally, parametric analysis of the model highlighted key parameters responsible for disease progression, which hold the potential to design new treatment strategies.