Integrated Sensing and Communication (ISAC) technology enables simultaneous sensing and communication functions through shared hardware and resources tailored to specific applications. This technology reduces hardware costs and enhances system performance through synergistic interactions. While Radio Frequency-based ISAC (RF-ISAC) is well-studied, Optical-based ISAC (O-ISAC) remains an emerging field. Compared with RF, light offers broader spectra, larger bandwidths, and flexible information transmission methods such as Orbital Angular Momentum (OAM). Thus, O-ISAC research focuses on exploiting these unique optical advantages to significantly improve integrated system performance. This paper provides a comprehensive review of O-ISAC systems from three distinct perspectives: fundamental theory, system optimization design, and potential applications. By revisiting relevant optical theories and introducing novel insights, we propose a comprehensive theoretical framework grounded in light field theory to characterize and analyze the spatial distribution and propagation mechanisms of light. Leveraging this foundation, O-ISAC can be realized by coordinating multidimensional resources and harnessing light’s inherent structural properties. We then systematically examine critical performance metrics of integrated systems and propose an optimization framework that highlights essential technologies and corresponding evaluation criteria. Finally, the application of O-ISAC is illustrated with examples drawn from vehicular networks and hybrid optoelectronic base station architectures.

Intelligent Transport Systems (ITS) are crucial for safety, efficiency, and reduced congestion in transportation. They require efficient, secure, high-speed communication. Radio Frequency (RF) technologies like Fifth Generation (5G), Beyond 5G (B5G), and Sixth Generation (6G) are promising, but spectrum scarcity mandates coexistence with Optical Wireless Communication (OWC) networks, which offer high data rates and security, forming a strong foundation for hybrid RF/OWC applications in ITS. In this paper, we delve into the application of Machine Learning (ML) to enhance data communications within OWC systems in ITS. We commence by conducting an in-depth examination of the data communication prerequisites and the associated challenges within the ITS domain. Subsequently, we elucidate the compelling rationale behind the convergence of heterogeneous RF technologies with OWC for data communications in ITS scenarios. Our investigation then pivots towards elucidating the indispensable role played by ML in optimizing data communications via OWC within ITS. To provide a comprehensive perspective, we systematically evaluate and compare a spectrum of ML methodologies employed in OWC ITS data communications. As a culmination of our study, we proffer a set of valuable recommendations and illuminate promising avenues for future research endeavors that warrant further exploration within this critical intersection of ML, OWC, and ITS data communications.