This study examines adult lifelong learning participation in Romania by analysing data obtained through the Adult Education Survey (AES), conducted within the framework of the European Statistical System. The analysis seeks to determine which demographic, social and economic elements affect both access to and involvement in education activities beyond the formal education, namely non-formal education and informal education.

The research methodology uses descriptive statistics alongside machine learning techniques:
- logistic regression and gradient boosting trees
- to both identify participation patterns and predict potential engagement in different educational activities.

The analysis outcomes will help develop better educational policies and ensure fair access to lifelong learning which supports personal and professional growth for individuals and communities. Recognizing the essential role of non-formal and informal education in lifelong learning is important for developing inclusive policies that address diverse learning needs and pathways.

Keywords: Education, Lifelong learning, Non-formal education, Informal education, Machine learning.