The human brain is an intricate biological structure, and a significant challenge in computational modelling arises from its multiscale spatio-temporal nature. This complexity involves multiple levels of increasingly sophisticated organization, from synapses to the entire brain, each requiring advanced algorithms and high-performance computing to simulate interactions and dynamics accurately. The complicated interaction inside this complex system influences the brain's capacity, which is determined by a specific mathematical and statistical framework. Advanced statistical modelling techniques incorporating coupling between physiological and pathological processes are required to advance state-of-the-art therapeutic strategies for treating neurodegenerative disorders (NDDs) like Alzheimer’s disease (AD) and Parkinson’s disease (PD), which impact millions globally. AD, in particular, is characterized by the accumulation of amyloid-beta (Aβ) plaques and tau (τ ) proteins, with misfolded proteins spreading through the brain by forming aggregates of varying size and toxicity. This study introduces an improved large-scale brain network model to determine the role of astrocytes in mitigating the spread of misfolded proteins in AD. By incorporating astrocytic clearance mechanisms and fragmentation dynamics, the model utilizes the Smoluchowski framework for nucleation, aggregation, and fragmentation to simulate the distribution and propagation of protein aggregates. Our findings reveal that astrocytic clearance varies with the size of the aggregate, playing a pivotal role in slowing the progression of AD. The model offers novel insights into the pathological processes underlying AD and highlights the importance of astrocytic functions by spanning multiple spatial and temporal scales. The integration of data-driven brain network models with detailed multiscale analyses advances the understanding of brain networks in health and disease, fostering the development of innovative strategies for treating and managing neurodegenerative disorders. Finally, detailed multiscale brain modelling offers a powerful framework for consolidating, organizing, and connecting the datasets underpinning data-driven brain network models.