This paper reviews the literature on Dynamic Difficulty Adjustment (DDA) in video games, focusing on mechanisms that adjust game difficulty based on players' performance, emotions, or personality. The review examines studies that employ machine-learning techniques, player modeling approaches, and various data types to assess players' states. The findings indicate significant effects of DDA on parameters such as enjoyment, flow, motivation, engagement, and immersion. Recent studies have shown increased attention to machine-learning and player modeling techniques in DDA design. However, more research is needed to better understand player preferences and cognitive characteristics, such as visual attention, working memory, and response time, to optimize game parameters more efficiently. The paper also outlines a three-step plan for DDA, including defining game difficulty, identifying player states, and determining the appropriate adjustment method.This paper reviews the literature on Dynamic Difficulty Adjustment (DDA) in video games, focusing on mechanisms that adjust game difficulty based on players' performance, emotions, or personality. The review examines studies that employ machine-learning techniques, player modeling approaches, and various data types to assess players' states. The findings indicate significant effects of DDA on parameters such as enjoyment, flow, motivation, engagement, and immersion. Recent studies have shown increased attention to machine-learning and player modeling techniques in DDA design. However, more research is needed to better understand player preferences and cognitive characteristics, such as visual attention, working memory, and response time, to optimize game parameters more efficiently. The paper also outlines a three-step plan for DDA, including defining game difficulty, identifying player states, and determining the appropriate adjustment method.