Cognitive Sleep Modulation through Generative AI and Real-Time Multi-Sensor Fusion introduces an intelligent, adaptive framework designed to improve sleep quality using advanced artificial intelligence techniques. The system gathers multi-modal physiological data—including electroencephalography (EEG), heart rate variability (HRV), respiratory signals, and body movement—from wearable and IoT-enabled devices. A real-time sensor fusion mechanism integrates these heterogeneous data streams and applies deep learning models to accurately classify sleep stages and detect disruptions. Based on the identified physiological state, generative AI algorithms produce personalized audio guidance, calming soundscapes, and cognitive relaxation prompts tailored to individual neural patterns. The framework dynamically adjusts environmental conditions such as lighting, sound, and temperature to facilitate smooth transitions across sleep cycles. Reinforcement learning strategies continuously optimize interventions by learning from long-term sleep efficiency metrics and user feedback. Experimental evaluations indicate reduced sleep onset latency, prolonged deep sleep phases, and improved sleep consistency. This intelligent, non-invasive solution demonstrates strong potential for personalized sleep enhancement and contributes to advancements in digital healthcare, cognitive science, and AI-driven wellness systems.