In a world where data never stops moving, the ability to act on information in real time has become a defining advantage. Financial systems must detect fraud the instant it happens, healthcare platforms must monitor patients continuously, and supply chains must adapt to disruptions as they unfold. Success no longer depends only on collecting data, it depends on transforming it into actionable intelligence in milliseconds. Artificial intelligence (AI) bridges this gap. By embedding AI inference into real-time data pipelines, organizations can move from reacting to events to anticipating them, making decisions that are faster, smarter, and more reliable. Harnessing AI Inference for Intelligent Decision-Making in Real-Time Dataflows examines how AI inference can be seamlessly embedded into real-time dataflows to enable faster, smarter, and more responsible decision-making. This book bridges theoretical advancements, emerging technologies, and real-world use cases, providing a roadmap for designing and managing AI-driven systems that operate at the edge of speed, scale, and responsibility. Covering topics such as smart cities, federated learning, and the internet of things (IoT), this book is an excellent resource for graduate and doctoral students, researchers, engineers, developers, system architects, business leaders, policymakers, and more.