The challenge of building systems that learn continuously and adapt without catastrophic forgetting is equally crucial. Human cognition accumulates knowledge over a lifetime, integrating new experiences while retaining previously acquired skills. Current artificial systems, by contrast, are typically trained once on static datasets and then frozen. Attempts to update them incrementally often lead to rapid degradation of earlier capabilities. Research in continual learning explores regularization techniques, dynamic architectures, and memory replay mechanisms that mitigate forgetting, yet no solution matches the robustness and efficiency of biological memory systems. Solving this problem is essential for deploying AI in dynamic real-world settings where data distributions shift and new tasks arise continuously.