Alzheimer’s disease is a multifactorial neurodegenerative disorder for which single-target pharmacological strategies have shown limited clinical success. The complexity of its underlying molecular mechanisms suggests that effective treatment may require the simultaneous modulation of multiple pathological pathways. In this context, drug combination therapies represent a promising alternative, although the experimental exploration of all possible combinations is unfeasible due to the vast combinatorial space. Network medicine provides a framework to address this challenge by modeling diseases and drugs within the human protein–protein interaction network and quantifying their topological relationships. In this study, we present a network-based pipeline to systematically evaluate potential drug combinations for Alzheimer by integrating drug–disease proximity and drug–drug separation metrics. We apply this framework to a large set of approved and investigational drugs in order to identify combinations that are both proximal to the disease module and topologically complementary. We specifically prioritize the combination of memantine and glimepiride, which exhibits an ideal complementary exposure pattern (sAB = 1.1). Our results reveal that while direct interaction with the disease module is limited (1.4%), this pair influences 356 unique proteins (13.2% of the total Alzheimer’s disease module) through 2-hop propagation paths. Overall, this framework identifies drug pairs that target complementary disease pathways, providing a clear biological rationale for developing new combination therapies for Alzheimer’s.