This paper investigates the application of Machine Learning models to predict user preferences based on their personality traits. The results of the conducted survey are utilized as input for estimations, with personality traits operationalized using the TIPI test - an abridged and validated version of the Five-Factor model - alongside risk perception as a sixth trait. The study proposes the implementation of three regression models - Linear Regression, Decision Trees, and Random Forest - with Random Forest appearing to be the most appropriate for this aim. The findings confirm the role of user personality and strengthen the reliability of Machine Learning models in making accurate predictions in this scientific domain. Finally, a conclusive overview of the research results is presented, demonstrating that personality significantly influences not only our decisions but also our thoughts, emotions, and behaviors in specific situations.