The convergence of population aging and cultural diversity demands new frameworks for understanding late-life mental health beyond traditional clinical models. Depression among older adults varies dramatically across cultures, yet healthcare systems often fail to account for how cultural beliefs about independence, emotional expression, and family roles influence both symptom presentation and treatment outcomes. The SUNSHINE framework—Seniors Uniting Nationwide to Support Health, INtegrated Care, and Evolution—represents an emerging approach that bridges cultural understanding with system-level interventions. This model recognizes that mental health outcomes result from complex interactions between deeply held cultural beliefs and structural healthcare capacity, rather than treating these as separate domains. The framework specifically addresses how fragmented care coordination, medication complexity, digital literacy gaps, and caregiver burden amplify cultural vulnerabilities in aging populations. Depression's financial impact on older adults creates additional spending disparities that reinforce systemic inequities, making whole-person and whole-system approaches essential rather than optional. Health information technology and artificial intelligence show promise for strengthening system resilience through culturally-informed early detection algorithms, personalized medication management, and improved care coordination across diverse populations. However, the effectiveness of these technological solutions depends heavily on their cultural calibration and integration with existing social infrastructure. The framework's emphasis on measuring both cultural variation and structural capacity represents a significant departure from traditional mental health metrics that focus primarily on individual symptomatology. This systems approach acknowledges that sustainable mental health improvements require addressing both the cultural contexts that shape older adults' experiences and the structural barriers that limit access to appropriate care.