The aging process is a complex multifactorial phenomenon, which is influenced by both external factors - climatic, economic and political conditions, and individual characteristics of the body. In this regard, modeling this process is a non-trivial task that requires a versatile approach to solve it. An analysis of the literature shows that when modeling the rate of aging, both conceptual [1-4] models are used, which give an idea of how to assess the aging process in principle, and more specific computational models [5-9], which make it possible to predict the rate of aging. When constructing computational models, there is a contradiction between the completeness of the model and the possibility of using it for forecasting. Thus, models that show all the relationships in the aging process well [7], which are usually constructed on graphs, are difficult to apply to the numerical estimation of the aging rate, although some of them make it possible to construct individual aging trajectories [8-9]. At the same time, models that have a strong numerical apparatus for estimating the rate of aging [5-6], as a rule, are sharpened to solve a narrow problem and do not cover the entire complexity of the aging process. In such a situation, the use of machine learning methods in computational models for estimating the rate of aging is a very promising direction [10-15], since its application allows us to take into account all the variety of factors of the aging process, without delving into the essence of the process itself. In this paper, machine learning methods are used to analyze the correlation of functional indicators of patients with their calendar age and to build models for predicting the biological age of patients. The data analysis was carried out with the help of the author's developments in the Python language in the Anaconda environment. For the analysis, we used functional indicators (10 pieces) of 1185 patients from the database of the clinical regional psycho-neurological hospital of war veterans in the number. The analysis of the data showed the presence of a statically significant correlation of the indicators used with the calendar age of the patients. In this paper, 5 regression models were constructed using various tools of the Python sklern library (batch gradient descent, stochastic gradient descent, ridge regression, ridge regression with Bayesian selection, the support vector machine method), and algorithm compositions from decision trees (random forest and boosting) were used. To improve the quality of the model, we used feature selection (add-dell) and outlier search and removal using the reference vector method, the isolating forest method, and the nearest neighbor method. All the models obtained are adequate (verification by the Fisher criterion), but the most accurate (R2 = 0.75) was shown by the model of the composition of a random forest on the full set of features after the removal of anomalies by the support vector machine. The results of modeling using linear models showed that the highest weights in the model have 3 functional indicators - accommodation, vital capacity of the lungs and hearing acuity.