Continual learning focuses on learning new tasks sequentially without forgetting previously acquired knowledge, while multi-task learning involves training a model on multiple tasks simultaneously. In multi-task learning, the model shares its parameters across tasks, which can lead to better generalization and improved performance. However, continual learning is more suitable for scenarios where tasks are encountered sequentially, and the model needs to adapt to new information without losing its ability to perform well on previously learned tasks.