Recent years have seen rapid progress in conversational AI, yet existing systems continue to struggle with producing factually correct and contextually appropriate responses in real time. This paper proposes a hybrid knowledge base architecture that integrates symbolic reasoning with neural representation learning to address these challenges. The design combines a symbolic knowledge graph for precise, verifiable query resolution with a transformer-based embedding module for contextual interpretation. A third layer supports dynamic data handling, enabling real-time updates and adaptation to evolving information streams. We implemented and evaluated the prototype on benchmark dialogue datasets, measuring accuracy, latency, and scalability. The hybrid system achieved significant improvements over both symbolic-only and neural-only baselines, particularly in multi-turn and domain-specific scenarios. Statistical analysis confirms that the observed gains in intent recognition, slot-filling, and response latency are consistent and robust. Beyond performance, the architecture offers practical benefits: symbolic components provide interpretability and traceability, while neural modules contribute flexibility and fluency. This balance enhances both conversational quality and user trust. Although deployment introduces challenges in computational overhead and integration complexity, the findings suggest that hybrid approaches offer a promising pathway toward real-time, reliable, and context-aware conversational agents for applications such as customer support, healthcare, and education.