Dynamic Difficulty Adjustment (DDA) is a method used in video games to automatically adjust game difficulty based on player performance, emotions, or personality. This systematic literature review examines DDA approaches, focusing on machine-learning techniques, player modeling, and data types used to assess player states. The review includes journal and conference articles published up to September 2022. The findings show that DDA significantly affects parameters such as enjoyment, flow, motivation, engagement, and immersion. Machine-learning and player modeling techniques have gained more attention in DDA design. However, more research is needed to better understand player preferences and adjust game parameters efficiently. Further research into players' cognitive characteristics, such as visual attention, working memory, and response time, could help in understanding player preferences better. The introduction outlines the importance of maintaining player engagement through personalized game features and adaptive AI agents. It also describes the three-step plan for difficulty adjustment: determining game difficulty variables, identifying player state variables, and selecting the appropriate difficulty adjustment method. The review highlights the need for consistent definitions of game difficulty and the importance of considering player personality traits in DDA systems.Dynamic Difficulty Adjustment (DDA) is a method used in video games to automatically adjust game difficulty based on player performance, emotions, or personality. This systematic literature review examines DDA approaches, focusing on machine-learning techniques, player modeling, and data types used to assess player states. The review includes journal and conference articles published up to September 2022. The findings show that DDA significantly affects parameters such as enjoyment, flow, motivation, engagement, and immersion. Machine-learning and player modeling techniques have gained more attention in DDA design. However, more research is needed to better understand player preferences and adjust game parameters efficiently. Further research into players' cognitive characteristics, such as visual attention, working memory, and response time, could help in understanding player preferences better. The introduction outlines the importance of maintaining player engagement through personalized game features and adaptive AI agents. It also describes the three-step plan for difficulty adjustment: determining game difficulty variables, identifying player state variables, and selecting the appropriate difficulty adjustment method. The review highlights the need for consistent definitions of game difficulty and the importance of considering player personality traits in DDA systems.