Racial identity, religiosity and neighborhood conditions were utilized to predict physical/mental health and life outcomes for a low income African American population. Data from 1,181 adult interviews, which were part of the Mobile Youth Survey (MYS) were engaged in a secondary data analysis to answer the research questions. For the analyses, a Hierarchical Linear Model (HLM) framework, implemented in SAS PROC MIXED using maximum likelihood (ML) methods was used. From the various models tested, six of the eight potential dependent variables yielded significant results: physical health change and mental health change were not significant. Results suggested that all three of the predictor variables (racial identity, religiosity and neighborhood conditions) are variables that are significant predictors of the dependent variables (health and life outcomes). Interesting patterns arose in relation to the specific scales used to measure each of the independent variables. There were distinct differences in the predictive patterns of the sub-scales for discrimination and racial identity, as well as emerging predictive trends for the sub-scales related to religiosity. Also, there is a discussion regarding future research to help determine whether racial identity, religiosity and neighborhood conditions are exclusive items or inseparable constructs for African American populations.