Artificial Intelligence (AI) has become an increasingly influential force in the domain of creative expression, with generative models now playing a significant role in music composition. These models, particularly those built on deep learning architectures, are capable of producing melodies, harmonies, rhythms, and full musical pieces in a variety of styles. While AI's contributions to music composition raise questions about authorship, originality, and artistic value, they also open new pathways for human-machine collaboration and creative augmentation. This paper explores the foundational technologies behind AI-generated music, including recurrent neural networks, transformers, and variation auto encoders. It investigates the use of generative models in both autonomous composition and cocreative systems. Use cases in film scoring, commercial music production, and educational tools are analyzed. Realworld applications and collaborations between musicians and AI systems are reviewed to understand the strengths and limitations of current technologies. Ethical and philosophical considerations surrounding creativity, intellectual property, and cultural representation are discussed. Finally, the paper considers the challenges of musical coherence, expressivity, and human-AI interaction, and outlines future directions in multimodal creativity, personalized composition, and real-time improvisation. AI-augmented music composition stands as a dynamic intersection of technology and art, expanding the boundaries of what it means to create music