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Beyond 5G and 6G communications are foreseen to transform the world, connecting not only people but also vehicles, wearables, devices, sensors, and even physical and digital worlds. To achieve that, 6G systems are expected to employ various disruptive technologies (e.g., non-terrestrial networks (NTNs), mmWave communications, pervasive artificial intelligence, and ambient backscatter communications) to enable/support new use cases, e.g., autonomous cyber-physical systems and Metaverse/holographic teleportation. Thus, this thesis aims to leverage the latest advances in machine learning (ML) to address different problems facing 6G systems. We first envision that UAVs will play a critical role in 6G and NTNs, e.g., flying data collectors. To tackle the uncertainty in the data collection process and the UAV’s energy capacity limitation, we propose an innovative deep reinforcement transfer learning approach to control the UAV's speed and energy replenishment process and allow UAVs to ``share'' and ``transfer'' learning knowledge, thus reducing learning time and improving learning quality significantly. 6G is also envisioned as ubiquitous sensors thanks to the Integrated Communications and Sensing (ICAS) technology, e.g., for flood sensing/warning or in autonomous vehicles (Avs). Optimizing the waveform structure for ICAS applications to AVs is one of the most challenging tasks due to the strong influences between sensing and data communication functions under dynamic environments. Therefore, we develop a novel framework that intelligently and adaptively optimize its waveform structure to maximize sensing and data …