Space-air-ground integrated network (SAGIN) has emerged as a paradigm shifting architecture that offers large-scale, flexible wireless coverage and seamless, high-rate connectivity to complement terrestrial communications. Nonetheless, unlocking the potentials of SAGIN is subject to addressing non-trivial challenges associated with their inherent time-variability, self-organization, and heterogeneity. Meanwhile, the concept of reconfigurable intelligent surfaces (RISs) is recognized as a disruptive technology that supports an unprecedented control of electromagnetic waves propagation and potentially offers significant enhancements in spectral efficiency, coverage expansion, and security, all achieved in a low-cost and energy-efficient manner. We anticipate that the integration of RISs into SAGIN will not only play a fundamental role in improving the quality of inter- and intra- layer communications, but will also provide complex interactions among the three network segments, and hence, opens the horizons for a new breed of applications across all industries. In this article, we explore the underlying opportunities and challenges of integrating RIS-enabled communications into SAGIN, and present a forward-looking overview of the cross-layer operational elements supported by RISs. Finally, we outline major enabling technologies and present a look ahead toward addressing open research issues.

Recent advances in programmable metasurfaces, also dubbed as reconfigurable intelligent surfaces (RISs), are envisioned to offer a paradigm shift from uncontrollable to fully tunable and customizable wireless propagation environments, enabling a plethora of new applications and technological trends. Therefore, in view of this cutting edge technological concept, we first review the architecture and electromagnetic waves manipulation functionalities of RISs. We then detail some of the recent advancements that have been made towards realizing these programmable functionalities in wireless communication applications. Furthermore, we elaborate on how machine learning (ML) can address various constraints introduced by real-time deployment of RISs, particularly in terms of latency, storage, energy efficiency, and computation. A review of the state-of-the-art research on the integration of ML with RISs is presented, highlighting their potentials as well as challenges. Finally, the paper concludes by offering a look ahead towards unexplored possibilities of ML mechanisms in the context of RISs.