Quantum-enhanced artificial intelligence (AI) is an emerging interdisciplinary field that harnesses quantum computing to augment machine learning and decision-making processes. This chapter provides a comprehensive overview of Quantum AI, from fundamental concepts to practical applications. We begin by outlining the motivations for integrating quantum computing with AI, highlighting how quantum phenomena like superposition and entanglement can potentially accelerate learning and computation. A review of background literature traces the development of quantum machine learning algorithms, including quantum neural networks, quantum support vector machines, and variational quantum eigensolvers. We then examine the theoretical foundations of Quantum AI, explaining how qubits, quantum gates, and hybrid quantum–classical architectures enable novel computational paradigms. The advantages of Quantum AI—such as parallelism and potential speed-ups in complex problem-solving—are weighed against current limitations including hardware noise, scalability challenges, and data encoding overheads. We also discuss ethical and security considerations, especially the implications of quantum-accelerated AI for data privacy and cryptography. Real-world use cases in finance, healthcare, and cybersecurity illustrate the transformative impact of Quantum AI in practice. Finally, we identify future research directions needed to fully realize the promise of quantum-enhanced decision-making. This chapter aims to equip readers with a clear understanding of the state-of-the-art in Quantum AI, providing technical depth in an accessible manner and laying out the next steps on the frontier of computing and intelligence.