This chapter provides a comprehensive overview of continual learning, a critical capability for artificial intelligence systems that must adapt to evolving data without forgetting previously acquired knowledge. It begins by introducing the concept and significance of continual learning in dynamic environments. The chapter then examines the phenomenon of catastrophic forgetting, where models lose performance on earlier tasks when trained on new ones. Various mitigation strategies are explored, including regularization techniques, memory-based approaches, and architectural modifications. The discussion extends to retraining strategies that aim to preserve knowledge across learning sessions and concludes with an overview of transfer learning as a complementary approach to enhance knowledge retention and generalization. Together, these sections offer a basic understanding of how AI systems can learn continuously and robustly over time.