This paper examines how cognitive-affective algorithms—advanced AI systems that interpret both emotional and cognitive user data—are transforming digital platforms into powerful engines of behavioral influence. By combining technologies such as multimodal machine learning, natural language processing (NLP), and deep reinforcement learning, these systems analyze real-time emotional cues and behavioral signals to optimize content delivery, user engagement, and decision outcomes. The study outlines the core mechanisms behind these algorithms, including emotional reinforcement loops and personalized content funnels, and explores their application across consumer behavior, political microtargeting, and social identity shaping. It also highlights pressing ethical concerns such as diminished user autonomy, invisible emotional manipulation, and the disproportionate impact on vulnerable populations like adolescents. Key regulatory and design recommendations include transparency mandates, algorithmic audits, emotional firewalls, digital literacy programs, and opt-out mechanisms for affective profiling. The paper concludes that cognitive-affective algorithms are not merely personalization tools—they are behavioral architects operating at scale. A multi-disciplinary response is essential to ensure that these systems serve humanity’s psychological well-being, democratic integrity, and digital rights.