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The therapies currently available mainly cater to alleviating the symptoms, whereas the newly approved disease-modifying antibodies, such as lecanemab and donanemab, bring out only limited clinical improvements. Being complicated and involving many factors, AD requires sophisticated computer-based methods to combine different biological data and find suitable therapy targets. In this review, we discuss how artificial intelligence (AI)-powered multi-omics data integration can be a catalyst in discovering drug targets, identifying biomarkers, and stratifying patients for AD. By utilizing machine learning techniques like random forests, graph neural networks, and deep learning, AI-led multi-omics methods have helped uncover new therapeutic targets. Models that were built using federated learning across various institutions outperformed single-center models with a higher area under the curve score (0.84, 0.94 versus 0.76, 0.85). AI-guided patient stratification lessened the clinical trial's sample size needs by 40, 55% while still retaining 80, 90% statistical power. Multi-omics analyses further pointed out that it is the downstream molecular pathways, and not amyloid pathology alone, that are significantly involved in disease progression, thereby questioning the effectiveness of single-target anti-amyloid therapies and endorsing combination treatment strategies. 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