INTRODUCTION/OBJECTIVE: The progressive nature of dementia is represented by a reduction in two or more cognitive functions, including language, memory, executive and visuospatial functioning, personality, and behavior. Only symptoms can be alleviated by traditional medication therapies. A strategy that shows promise for focused and customized interventions is gene therapy, especially when combined with artificial intelligence (AI).This review investigates the integration of AI with gene delivery technologies to enhance the accuracy, effectiveness, and personalization of dementia therapies. METHOD: PRISMA guidelines were followed in conducting a systematic review. A literature search for English-language articles published between 2012 and 2026 was conducted using PubMed, Scopus, and Google Scholar. Peer-reviewed original research, computational studies, or reviews; attention to AI in the context of gene delivery in dementia; and pertinence to biological mechanisms or therapeutic approaches were the inclusion criteria. Non-English papers, conference abstracts, letters, editorials, and research unrelated to gene delivery or dementia were among the exclusion criteria. The full texts, abstracts, and titles were vetted by two separate reviewers. Discussions or seeking advice from a third reviewer were used to settle disagreements. RESULTS: AI has proven to be very helpful in predicting therapeutic targets, optimizing gene vectors, and customizing dementia treatment plans. Using machine learning and deep learning approaches, it also makes gene-target prediction, nanocarrier optimization, and post-treatment monitoring easier. These methods improve vector design, find biomarkers, and decode complex genomic data. Precision gene delivery across the blood-brain barrier is now possible because to integration with (Clustered Regularly Interspaced Short Palindromic repeat) CRISPR/Cas9, adeno-associated virus vectors, and lipid nanoparticles, which has increased transfection efficiency and decreased cytotoxicity. CONCLUSION: Precision medicine is becoming possible because of AI frameworks that integrate neuroimaging, multi-omics data, and patient-specific genomic profiles. Notwithstanding the potential, there are still issues including data security, ethical dilemmas, translational barriers, and legal limitations.