Neurodegenerative diseases such as Alzheimer’s and Parkinson’s are highly complex, heterogeneous, and multifactorial. Their progression unfolds over many years, driven by diverse biological and environmental factors, and symptoms can vary significantly between individuals. These characteristics make diagnosis and treatment particularly challenging. Traditional diagnostic tools and experimental approaches, though invaluable, often fall short in capturing the full scope of disease mechanisms or tailoring effective therapies. Therapeutic responses also differ widely among patients, and ethical and practical constraints further complicate experimental design and implementation. While experimental studies have revealed critical insights into these diseases, they often struggle to fully represent the intricate progression and variability of treatment outcomes. Mathematical and computational models offer a powerful complement to experimental research. By integrating data across molecular, neural circuit, and behavioral levels, these models create unified frameworks that simulate disease processes across multiple scales. This enables deeper understanding of underlying mechanisms, prediction of long-term outcomes, and optimization of diagnostic and therapeutic strategies.