Objective: Alzheimer's disease (AD) continues to be the leading cause of dementia worldwide, due to its complicated, multifactorial pathophysiology. To shift the AD paradigm from reactive to proactive, from a specific time to a precision medicine intervention strategy, this study examines the role of artificial intelligence (AI) as a potential linking point for multi-omics data, new fluid biomarkers, and developing medicines. Methods: A thorough search was conducted in clinical trial registries (ClinicalTrials.gov) and peerreviewed literature (PubMed, Scopus). Without going too far into detail, this study discussed novel anti-amyloid/anti-tau monoclonal antibody treatments, blood biomarkers (p-tau217, p-tau181), and machine learning approaches for early diagnostic classification and for prediction modelling. Results: Recent breakthroughs in understanding that different disease-modifying treatments, such as lecanemab and donanemab, can stop cognitive decline by targeting specific types of disease-causing aggregates have greatly shortened diagnostic time through blood-based diagnostic markers. Importantly, AI systems (e.g., structural MRI and multi-omics-based panels analyzed using Deep Learning architectures) can predict conversion from Mild Cognitive Impairment (MCI) to overt AD more than a year before the onset of clinical symptoms, with accuracy exceeding 90%. Sensitivity of real-world trials is significantly enhanced by the use of Digital Biomarkers and remote cognitive monitoring. Discussion: As a tool, AI is not discrete but serves as a coherent framework that can progressively connect data from different biological systems (e.g., upstream molecular events such as amyloid cascades, tauopathy, neuroinflammation) with downstream clinical events (phenotypes). The lack of standardized datasets across different ethnic groups, algorithm interpretability (the "black box" problem), and possible regulatory validation, however, is now preventing the full clinical translation. Conclusion: With insulin injections, or even just the excitement of a new drug, the world of AD research is transformed. The multimodal AI frameworks provide clinicians with ways to treat patients with highly personalized, multi-targeted treatment strategies to capture AD pathology at its asymptomatic early stage, by simultaneously computing genetic risk, neuroimaging features, and plasma biomarker levels. DOI:10.2174/0113894501504715261014100550