<![CDATA[The increasing adoption of electronic voting (e-voting) systems has improved electoral efficiency and accessibility while simultaneously introducing new challenges related to cybersecurity and electoral data integrity. This study aims to examine the integration of Artificial Intelligence (AI) in e-voting systems for anomaly detection and the prevention of electoral data manipulation. Using a library research approach, data were collected through the analysis of books, scientific journals, research reports, and other relevant academic literature related to Artificial Intelligence, anomaly detection, and electoral data integrity. The findings indicate that AI-based anomaly detection mechanisms can effectively identify unusual patterns, suspicious activities, and potential manipulation attempts within electoral datasets. Furthermore, machine learning algorithms enable continuous monitoring and adaptive threat detection, overcoming several limitations of traditional security approaches. The study concludes that integrating AI into e-voting systems can strengthen electoral data integrity, improve security performance, and enhance public trust in digital electoral processes while supporting transparent, reliable, and accountable democratic governance.]]>
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