<p>The rapid growth of IoT applications has generated significant challenges in efficient content storage and retrieval within dynamic environments. Traditional storage approaches often fail to adapt to changing network conditions and scalability requirements. To address this issue, this paper proposes an intelligent framework for optimizing content storage that combines Deep Reinforcement Learning (DRL) and the Virus Colony Algorithm (VCA). In the proposed framework, DRL is employed to support adaptive cache management decisions based on network conditions, while VCA optimizes content placement to improve resource utilization and reduce access latency. The hybrid approach enables more efficient management of content storage in dynamic IoT environments and supports timely adaptation to changing user requests and network states. Simulation results demonstrate that the proposed method improves the cache hit ratio by 15%, reduces network load by 20%, and decreases data access time by 20% compared with conventional approaches. These results indicate that the proposed framework can effectively support scalable and intelligent content storage management in dynamic IoT networks.</p>