This initiative, is part of a larger effort led by Prof. Javed Mostafa from the School of Information, focuses on developing an integrated and inclusive mental healthcare model that addresses diverse community needs. Biostatistical and AI-approaches for Longitudinal Analysis in Neuropsychiatric Care Evaluation (BALANCE), as the name implies, uncover significant predictors of mental health outcomes, such as socio-economic factors and environmental stressors, while identifying complex, non-linear patterns within large datasets. These tools enable us to design predictive models for mental health trends and develop knowledge-based systems that support proactive, data-driven interventions. Example projects include AI-powered tools for early detection of mental health issues, and models assessing the impact of one or many mental health diagnoses on end-of-life experience. By advancing rigorous, inclusive approaches to data management and utilization, we aim to promote equitable mental health outcomes and empower community-focused care.