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Integrated Sensing and Communication (ISAC), combined with data-drivenapproaches, has emerged as a highly significant field, garnering considerableattention from academia and industry. Its potential to enable wide-scaleapplications in the future sixth-generation (6G) networks has led to extensiverecent research efforts. Machine learning (ML) techniques, including$K$-nearest neighbors (KNN), support vector machines (SVM), deep learning (DL)architectures, and reinforcement learning (RL) algorithms, have been deployedto address various design aspects of ISAC and its diverse applications.Therefore, this paper aims to explore integrating various ML techniques intoISAC systems, covering various applications. These applications spanintelligent vehicular networks, encompassing unmanned aerial vehicles (UAVs)and autonomous cars, as well as radar applications, localization and tracking,millimeter wave (mmWave) and Terahertz (THz) communication, and beamforming.The contributions of this paper lie in its comprehensive survey of ML-basedworks in the ISAC domain and its identification of challenges and futureresearch directions. By synthesizing the existing knowledge and proposing newresearch avenues, this survey serves as a valuable resource for researchers,practitioners, and stakeholders involved in advancing the capabilities of ISACsystems in the context of 6G networks.

Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects