The aim of this paper was to assess satisfaction with the quality of life in the context of machine learning and to develop a model using machine learning algorithms. The theoretical section provides a description of the concept of machine learning and an analysis of machine learning approaches and algorithms. Additionally, it offers insight into topics related to mental health and quality of life. In the practical section, research was conducted through the application of machine learning to predict satisfaction with quality of life in the student population. Specifically, a random forest regression model was used. The main goal of the research was to identify key factors influencing students' satisfaction with quality of life and to assess the effectiveness of machine learning in providing sufficiently accurate predictions that could help inform and create appropriate strategies to improve student health and quality of life. For the purposes of this paper, a random forest regression model was developed and evaluated, demonstrating a relatively high level of accuracy in prediction. The model identified several key factors affecting the quality of life: sleep quality, social life, physical activity, and personal safety. These findings indicate the multifaceted nature of the concept of quality of life, especially within the student population, emphasizing the importance of improving certain aspects of their lives to enhance overall satisfaction with the quality of life. The insights gained from the research further highlight the potential of machine learning models in developing strategies that can significantly improve the quality of life for students.