(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The growing demand for intelligent automation across sectors such as autonomous transportation, financial fraud detection, smart energy grids, and emergency healthcare response has positioned real-time decision-making at the center of modern artificial intelligence research. Delivering accurate decisions within stringent temporal bounds requires not only performant models but also a holistic system architecture capable of end-to-end scalability, resilience, and ethical alignment. This paper presents an interdisciplinary examination of the design principles and optimization strategies that underpin scalable AI systems for real-time inference and control. We analyze the architectural trade-offs among centralized cloud, edge, and hybrid deployments, and discuss how stream processing, model serving, and hardware-aware compilation form a layered decision pipeline. The discussion extends to data infrastructure challenges, including in-motion processing, state management, and concept drift handling, as well as to model optimization through lightweight architectures, quantization, and knowledge distillation. Beyond performance, the paper foregrounds system-level governance, fairness, sustainability, and policy dimensions that must be integrated from the earliest stages of design. Through cross-domain case illustrations—spanning live video analytics, financial markets, and autonomous systems—we highlight how structural choices in system design directly influence latency, throughput, energy consumption, accountability, and compliance with emerging regulatory frameworks. The paper concludes with a forward-looking perspective on standardization, interoperability, and the need for resilient architectures that gracefully degrade under uncertainty, ultimately arguing that scalable real-time AI can only be achieved through the co-optimization of computational, social, and institutional infrastructures.