Algorithmic Media Theory focuses on the pivotal role algorithms play in curating, shaping, and controlling media consumption. With the proliferation of digital platforms, algorithms have become gatekeepers of information and entertainment, influencing what users see, read, and hear. Historical examples provide key insights into the evolution of algorithmic influence. For instance, YouTube’s recommendation engine, introduced in 2008, transformed how content is consumed, encouraging prolonged engagement by suggesting related videos tailored to user preferences. Similarly, Spotify’s Discover Weekly playlists leverage user data to curate music recommendations, blending personalization with discovery. These examples illustrate the power of algorithms to shape user behavior, redefine media consumption patterns, and influence cultural trends. However, Algorithmic Media Theory also raises ethical concerns. Algorithms often lack transparency, making it difficult for users to understand how their data is used or why specific content is prioritized. Bias embedded in algorithms can perpetuate inequality, as seen in cases where AI-driven systems amplify harmful stereotypes or misinformation. This chapter critically examines these issues, encouraging readers to reflect on the ethical implications of algorithmic control in shaping media landscapes.