Continual Learning is a machine learning approach where models are designed to learn from new data over time without forgetting previously acquired knowledge. This method allows systems to adapt to changing environments and improve their performance as they encounter new information, making them more flexible and efficient. Unlike traditional learning methods that require retraining from scratch, Continual Learning focuses on incremental updates. This is particularly useful in applications like robotics, natural language processing, and autonomous vehicles, where ongoing learning is essential to handle dynamic and evolving tasks effectively.