Decision support systems increasingly depend on machine learning models that operate under uncertainty, fast streaming conditions, and complex regulatory constraints. Purely data driven approaches often struggle with distribution shift, incomplete data, and the need for transparent justification of recommendations in high stakes environments. Symbolic reasoning, in contrast, offers explicit structure, but it is hard to scale and adapt to noisy signals. This article proposes a hybrid intelligence framework that integrates neural representation learning with symbolic knowledge models for robust decision support in complex systems. The framework combines lightweight deep models for pattern extraction with rule based and logic driven components for constraint enforcement and explanation. Building on advances in adaptive learning, edge intelligence, and explainable artificial intelligence, the work specifies an architecture that separates perception, abstraction, and reasoning layers while maintaining tight feedback connections between them. A simulated decision support scenario in healthcare inspired environments illustrates the integration of neural predictors with symbolic policies and uncertainty aware aggregators. Experimental results show that the hybrid approach improves stability under drift, supports traceable recommendations, and reduces catastrophic errors when compared with stand alone neural baselines. The article contributes a design pattern, mathematical formulation, and empirical study that demonstrate how neural symbolic integration can strengthen decision support in complex technical and organizational systems.