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Population ageing has intensified long-term care demands globally, necessitating innovative, data-driven, and technology-enabled approaches to support caregivers. This study aimed to assess mental health and quality of life among caregivers of older adults in the Northeast of Thailand and to examine structural relationships among key determinants. A cross-sectional analytical study was conducted between January and August 2025 among 438 caregivers using multistage stratified random sampling. Standardized instruments were used. Multivariate logistic and linear regression analyses, along with Structural Equation Modeling were conducted to identify predictors and examine direct and indirect pathways. The prevalence of depression was 24.2% (95% CI: 20.2-28.5). High caregiver burden (AOR = 3.48, 95% CI: 2.12-5.71), poor social support (AOR = 3.02, 95% CI: 1.82-5.01), and low income (AOR = 2.31, 95% CI: 1.29-4.14) were significantly associated with depression. Social support positively predicted QoL (β = 0.38, p < 0.001), while depression negatively influenced QoL (β = -0.44, p < 0.001). SEM demonstrated good fit (CFI = 0.95, RMSEA = 0.045), confirming depression as a mediator. Caregiver burden and inadequate social support emerged as key determinants, highlighting opportunities for integrating predictive analytics and digital health interventions in long-term care systems.