Single-paradigm AI solutions no longer sufficiently address the complexity, scale, and dynamics of modern intelligent systems. When deployed in isolation, traditional paradigms often encounter limitations related to scalability, energy efficiency, latency, interpretability, or robustness. The future of AI lies in integration through hybrid architectures that unify complementary strengths across computational, algorithmic, and infrastructural domains. Hybrid systems promote adaptive coordination between modalities, infrastructures, and learning paradigms. Further exploration may reveal how hybridization enables systems to respond dynamically to environmental feedback and evolving workloads. Hybrid AI Architectures for Intelligent Systems explores the conceptual foundations, architectural innovations, performance optimizations, and future directions of hybrid AI systems. It examines how hybrid AI redefines current frameworks for future intelligent systems. This book covers topics such as cyber threat detection, edge computing, and smart cities, and is a useful resource for engineers, academicians, researchers, and scientists.