<p>Predicting the fatigue life of metal alloys under multiaxial loading is an urgent task in modern engineering. Established methods for predicting durability often do not provide the required accuracy. Given this, it is important to study new approaches, particularly those based on machine learning methods, which are becoming key tools that allow scientists and engineers to accelerate the development of new materials, processes, and technologies. The paper discusses such machine learning models as decision trees, ensemble models, Gaussian process regression, and artificial neural networks in terms of their application to predicting the multiaxial fatigue life of metal alloys. A database has been created that includes test results for ten metal alloys under different loading trajectories (226 test results under proportional and nonproportional multiaxial loading). The study analyzed 31 criterion parameters traditionally used in multiaxial fatigue models and integrated them into machine learning models as input variables, with durability as an output parameter. The impact of different groups of variables as input parameters of machine learning models on their performance is discussed. The machine learning models are compared with two multiaxial fatigue models, namely the Fatemi–Socie model and the modified Brown–Miller model proposed by the authors. The study’s results showed that using machine learning methods, in particular artificial neural networks and Gaussian process regression, allowed us to achieve greater accuracy in predicting the durability of materials under multiaxial loading compared to analytical models. It is shown that the introduction of machine learning into the practice of predicting durability under multiaxial loading provides exceptional possibilities for highly reliable calculations.</p>