This paper tackles catastrophic forgetting in continual learning for multimodal knowledge graphs, where evolving structures and heterogeneous modalities challenge existing methods. We propose ARTD, a novel framework integrating adaptive memory replay and topology-aware knowledge distillation. Our approach dynamically balances new knowledge acquisition with historical knowledge preservation through: task-sensitive memory sampling guided by real-time forgetting metrics, multi-level distillation preserving structural relationships via contrastive alignment and parameter regularization, and cross-modal gating for unified entity representations. Comprehensive evaluation across academic and industrial benchmarks demonstrates superior performance over state-of-the-art baselines in link prediction tasks. Ablation studies confirm the complementary nature of replay and distillation mechanisms, while temporal analysis shows effective mitigation of forgetting across incremental learning phases. The framework’s robustness to domain-specific heterogeneity establishes its viability for real-world dynamic knowledge systems.