<![CDATA[The rapid advancement of digital information systems has increased the need for intelligent technologies capable of analyzing and predicting user behavior effectively. Artificial Intelligence (AI) has emerged as one of the most significant technologies for enhancing system intelligence, personalization, operational efficiency, and data-driven decision-making processes. This study aims to analyze the implementation of Artificial Intelligence for user behavior prediction in digital information systems. The research employs several AI algorithms, including Decision Tree, Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM), to evaluate predictive performance in analyzing user interaction data. The datasets used in this study consist of browsing history, transaction records, click frequency, session duration, login activities, and user preferences collected from digital platforms. The research process includes data collection, preprocessing, algorithm implementation, predictive analysis, and performance evaluation. The results indicate that AI-based predictive systems successfully improve behavioral prediction accuracy, personalization capabilities, cybersecurity monitoring, and operational effectiveness. Among all implemented algorithms, the Long Short-Term Memory (LSTM) model achieved the highest predictive accuracy due to its capability in analyzing sequential behavioral patterns. Furthermore, the findings demonstrate that AI implementation significantly contributes to the development of adaptive and intelligent digital information systems. Despite challenges related to privacy, computational complexity, and model interpretability, Artificial Intelligence provides substantial advantages for modern digital ecosystems and supports the advancement of intelligent user-centered services in the era of digital transformation.]]>
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