Transfer learning is a broad concept where a model trained on one task is reused for another, while fine-tuning is a specialized form of transfer learning. Whether transfer learning is “better” depends on the context.
If your domain is closely related to the pre-trained model’s data, fine-tuning can be highly effective. However, if your task requires completely new knowledge, transfer learning with pre-training from scratch might be a better option.