This study proposes an artificial intelligence-driven secure semantic communication system. Its goal is to enable safe, real-time communication while preserving the meaning of messages. The system uses advanced encryption methods, such as AES-based semantic encryption and homomorphic encryption, together with reinforcement learning (RL) to dynamically adjust encryption. The system architecture comprises several modular tiers. Data preparation, semantic analysis, dynamic encryption adjustment, and safe transmission are all parts of these layers. To test the system’s encryption, security, and adaptability, extensive research was conducted across different network conditions, including excellent, middling, and poor connections. The results show that the system can maintain low latency in real-time communication, maintain both data secrecy and semantic integrity, and dynamically adjust the encryption level based on real-time network conditions. The system’s capacity to keep communication private shows that it has all of these features. This solution is perfect for applications that require secure communication with minimal delay. Some examples of these uses include mobile communications, critical infrastructure, and networks for the Internet of Things.