The sustained increase in emotional disorders and suicidal behaviors among young people aged 18 to 25 has become a global priority concern for educational, health, and social systems. In light of this situation, it is urgent to develop effective tools for the early detection of emotional distress and the prevention of suicide risk. This paper presents an applied research study that explores the potential of subjective variables —such as happiness and optimism about the future— as early indicators of young people’s emotional well-being and possible signs of vulnerability.

Based on the analysis of publicly accessible open data from opinion barometers, the study assesses the relationship between these variables and suicide rates in Spain, using Machine Learning and Artificial Intelligence techniques. The study makes it possible to identify useful patterns for early detection in contexts where timely intervention can make a significant difference.

This approach, based on open data and automated analysis techniques, offers a complementary way to monitor the emotional climate of young people. Its results may serve as a basis for future prevention strategies in educational settings, without the need for intrusive interventions or additional resources. The work is framed within an interdisciplinary perspective that connects psychology, technology, and education in the promotion of mental health.

Keywords: Happiness, optimism, youth, suicide prevention, artificial intelligence, open data, emotional well-being.