Major depressive disorder (MDD) is a prevalent and disabling mental health condition traditionally diagnosed through subjective clinical interviews and retrospective self-reports, methods that are limited by recall biases and diagnostic heterogeneity. To address these limitations, ecological momentary assessment (EMA) and voice-based analysis have emerged as innovative diagnostic and monitoring tools. EMA captures real-time, context-rich data in naturalistic settings, providing nuanced insights into the dynamics of depressive symptoms, daily stressors, and coping strategies. Voice analysis leverages quantitative acoustic and linguistic biomarkers, reflecting underlying neurophysiological and psychomotor changes characteristic of depressive episodes. Integrating these technologies offers objective, scalable, and real-time approaches to enhance diagnostic accuracy, personalize interventions, and facilitate continuous patient monitoring. Nevertheless, practical challenges, including technological accessibility, participant adherence, data interpretation complexities, ethical concerns, and the necessity for robust validation, remain critical barriers. Future research directions highlight the need for digital phenotyping strategies using big data analytics to redefine depressive disorders beyond conventional DSM frameworks, ultimately paving the way for precision psychiatry.