Abstract: The dry purification of uranium hexafluoride(UF₆) is a crucial step in the nuclear fuel cycle. The process exploits the differences in volatility between crude UF₆ and impurity fluorides, such as WF₆, MoF₆, and VF₅, to achieve effective purification, which is essential for the efficient utilization of nuclear energy and the reduction of environmental impacts. Compared with conventional wet conversion routes, the all-dry distillation process avoids the use of organic solvents, thereby eliminating the generation of solvent-containing secondary liquid waste and reducing radioactive waste generation. Consequently, all-dry distillation has attracted increasing attention in the nuclear industry. However, due to the intrinsic properties of the materials and the presence of HF, the all-dry UF₆ distillation purification process typically operates under highly corrosive conditions, complex process regimes, and stringent purity requirements, imposing high demands on equipment performance, process control, and long-term operational reliability. The specificity of the UF₆ system results in a scarcity of fundamental thermodynamic data, such as vapor-liquid equilibrium information, which constrains the accurate description of phase behavior and limits process design, simulation, and optimization. Furthermore, the separation efficiency, pressure-drop characteristics, mass-transfer performance, and long-term stability of packing materials have not been systematically validated under highly corrosive environments and elevated operating pressures. Consequently, packing selection, structural design, and column scale-up may remain highly dependent on empirical judgment. In addition, the inherently high energy consumption of distillation requires effective strategies for improving separation efficiency and reducing energy consumption while maintaining stringent product purity. The pronounced response lag of the distillation column to disturbances in feed composition, flow rate, and temperature also poses challenges to operational stability and product quality consistency. Overall, the purification process faces several key challenges, including insufficient thermodynamic data, uncertainty in packing performance, high energy consumption, and limited operational stability. These challenges collectively restrict the further improvement of purification efficiency, process reliability, and intelligent operation. In recent years, artificial intelligence(AI), particularly machine learning(ML) and deep learning, has demonstrated increasing potential for addressing complex problems in chemical engineering through data-driven modeling, nonlinear relationship identification, prediction, optimization, and intelligent control. AI-based approaches have been applied to thermodynamic property prediction, equipment and material performance evaluation, process optimization, energy-efficient operation, soft sensing, fault diagnosis, and advanced process control. Against this background, this review systematically examines the potential applications of AI in all-dry UF₆ distillation purification from four key perspectives: thermodynamic data prediction, packing material and structure optimization, energy-efficient separation process design, and distillation operation and fault diagnosis. Regarding thermodynamic data prediction, machine learning models, including neural networks, random forests, and physics-informed neural networks, can capture nonlinear relationships and predict key properties such as vapor-liquid equilibrium and activity coefficients. For packing performance and structural optimization, machine learning models can predict pressure drop and mass-transfer performance, while inverse design and advanced manufacturing offer new approaches for developing enhanced packing structures. In energy-efficient separation design, artificial neural networks can identify energy-efficient distillation sequences and characterize the relationship between operating conditions and energy consumption. For distillation operation and fault diagnosis, deep learning models such as convolutional neural network(CNN) can facilitate abnormal-condition detection, fault identification, and intelligent process monitoring. By systematically reviewing research progress, methodological approaches, and potential limitations in these areas, this review provides a theoretical and methodological basis for AI, particularly ML, applications in all-dry UF₆ distillation purification. It also identifies potential research directions and highlights opportunities for further AI-enabled development in model development, process optimization, intelligent control, UF₆ distillation purification and nuclear chemical engineering.