Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and cholinergic dysfunction, with no disease-modifying therapy currently available. Acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) play critical roles in acetylcholine degradation, and dual inhibition of these enzymes has emerged as a promising therapeutic approach, particularly in advanced stages where BuChE activity predominates. This proposal aims to design and develop novel dual AChE/BuChE inhibitors through a comprehensive AI- and machine learning (ML)-driven drug discovery pipeline. The study will integrate deep learning-based virtual screening of ultra-large chemical libraries with structure-based docking, MM/GBSA binding energy calculations, molecular dynamics (MD) simulations, and DFT-based electronic structure analysis to identify potent and stable dual inhibitors. Natural product scaffolds and rationally designed derivatives will be included to explore chemical diversity. Selected candidates will undergo ADME/Tox prediction and multi-objective optimization to ensure CNS drug-likeness and favorable pharmacokinetic profiles. Top-ranked compounds will be synthesized and structurally characterized, followed by in vitro enzymatic inhibition assays against human AChE and BuChE to determine IC₅₀ values and selectivity. Further, their neuroprotective, anti-inflammatory, and anti-Aβ aggregation properties will be evaluated. In vivo efficacy will be evaluated using well-established mouse models of Alzheimer’s disease, including AlCl₃- and scopolamine-induced cognitive impairment. Behavioral assessments such as the Morris water maze, Y-maze, and novel object recognition tests will be employed to measure learning and memory performance. Additionally, biomarker analyses will be conducted to evaluate neuroprotective, anti-inflammatory, and anti-amyloid effects of the lead compounds. This integrative pipeline is expected to deliver structurally novel, multifunctional, dual cholinesterase inhibitors with strong therapeutic potential, bridging AI-driven in silico discovery with experimental validation for future AD therapy.