Behavioral health disorders, specifically depression, are a serious health concern in the United States and worldwide. The consequences of unaddressed behavioral health conditions are multifaceted and have impact at the individual, relational, communal, and societal level. Despite the number of individuals who could benefit from treatment for behavioral health concerns, their difficulties are often unidentified and unaddressed through treatment. Technology carries unrealized potential to identify people at risk for behavioral health conditions and to inform prevention and intervention strategies. Drawing upon data from the National Longitudinal Study of Adolescent Health (Add Health, n=3782), this study has two aims related to advancing understanding of technology’s potential value in behavioral health: 1) to develop a forecasting procedure that can be used to identify youth who are at risk of reporting a depression diagnosis as adults based on a set of input variables; and 2) to understand the developmental trajectories of depression for youth. To address the first aim of this study, random forest methodology was used to derive the forecasting algorithm. The second aim was pursued with Generalized Additive Model analysis to estimate relationships between presence of a reported depression diagnosis as an adult and youth characteristics. Findings from this study indicate that it is feasible to use a forecasting tool to identify individuals at risk of being diagnosed with depression, which can facilitate early intervention and improved outcomes. Gender, race, and receiving counseling as a youth were the most important predictors of having a reported depression diagnosis as an adult. This dissertation addresses the role of health disparities, specifically gender and race, related to depression and mental health treatment. In sum, this dissertation highlights how a machine learning forecasting tool could be used to inform prevention strategies and understanding of factors associated with receiving a depression diagnosis. This study presents and discusses these findings in addition to offering important implications for future research and practice to identify and prevent behavioral health conditions such as depression.