  Deep learning (DL) has revolutionized wireless communication systems by
introducing datadriven end-to-end (E2E) learning, where the physical layer
(PHY) is transformed into DL architectures to achieve peak optimization.
Leveraging DL for E2E optimization in PHY significantly enhances its
adaptability and performance in complex wireless environments, meeting the
demands of advanced network systems such as 5G and beyond. Furthermore, this
evolution of data-driven PHY optimization has also enabled advanced semantic
applications across various modalities, including text, image, audio, video,
and multimodal transmissions. These applications elevate communication from
bit-level to semantic-level intelligence, making it capable of discerning
context and intent. Although the PHY, as a DL architecture, plays a crucial
role in enabling semantic communication (SemCom) systems, comprehensive studies
that integrate both E2E communication and SemCom systems remain significantly
underexplored. This highlights the novelty and potential of these integrative
fields, marking them as a promising research domain. Therefore, this article
provides a comprehensive review of the emerging field of data-driven PHY for
E2E communication systems, emphasizing their role in enabling semantic
applications across various modalities. It also identifies key challenges and
potential research directions, serving as a crucial guide for future
advancements in DL for E2E communication and SemCom systems.
