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U-Mo alloy is with great development potential as a kind of dispersive fuel in research and test reactors. Improving the efficiency of powder obtention via hydride-dehydride process is a prerequisite for efficient powder metallurgy preparation of U-Mo alloy dispersion fuels. Optimizing parameters such as homogenization temperature, isothermal aging temperature, isothermal aging time, and Mo content is beneficial to increase the α-phase content of U-Mo alloys, thereby improving the efficiency of the power obtention of U-Mo alloy. Machine learning aided design of materials can greatly reduce the trials of expensive and time-consuming experiments and improve the efficiency of material development. In this paper, a machine learning method is applied to the rapid design of isothermal decomposition parameters of U-Mo alloys. With the hardness of the alloy as a design index, a machine learning support vector machine (SVM) model between the alloy hardness and the above parameters is established based on a small amount of data. Based on the prediction of hardness, the differences in optimization efficiency between the two types of experimental design algorithms based on predicted values and based on expected improvement are compared. The results show that the experimental design algorithm based on the expected improvement can significantly improve the hardness through a small number of iterative experiments, while the design algorithm based on the predicted value does not significantly improve the hardness. Using the above-mentioned machine learning aided design method, the optimal parameter combination for isothermal decomposition of the alloy was successfully determined through 4 experiments. When the aging temperature is 565 °C, the aging time is more than 20 h, the homogenization temperature is 900~950 °C, and the Mo content is 6wt.%, the hardness of the alloy processed is the highest, and the powder obtention rate is the highest. This study made a preliminary attempt to use machine learning methods to quickly optimize U-based alloy process parameters. Such data-based methods can effectively improve the efficiency of material development.