The global increase in dementia cases highlights the importance of early detection and intervention, particularly for individuals at risk of mild cognitive impairment (MCI), a precursor to dementia. The aim of this study is to develop and validate machine learning (ML) models based on non-imaging features to predict the risk of MCI conversion in cognitively healthy older adults over a three-year period. Using data from 845 participants aged 65 to 87 years, we built five eXtreme Gradient Boosting (XGBoost) models of increasing complexity, incorporating demographic, self-reported, medical, and cognitive variables.