The paper presents the results of a research of the possibility of assessing a person’s well-being using gait parameters. Gait parameters were assessed using smartphone accelerometer data collected during the research from 2023. The work also describes the features and methodology for collecting and preprocessing data. Acsa Active and Data Analyzer proprietary software was used to collect and process data. In addition to recording the gait, the person entered data about his well-being. This paper describes the results of predicting well-being based on gait parameters using the example of headache and lower back pain. The simulation was performed in the TensorFlow package. The comparative analysis included: feedforward neural networks (with the number of layers from 2 to 5), vector clustering, K-nearest neighbors, random forest, gradient boosting, ada boosting, K-nearest neighbor-based bagging classifier and gradient-based competitive network boosting, random forest and linear regression. According to the results of comparison of the described forecasting algorithms, the best results were shown by the competitive network (F-score is 0.75), bagging (F-score is 0.74), multilayer perceptron (F-score is 0.71). The results of the research indicate the possibility of forming predictive estimates of changes in a person’s well-being based on data on his gait collected using a smartphone. The work also notes that to understand all the capabilities of the technology for assessing health by gait using a smartphone, it is necessary to expand the sample and attract additional data.