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Facial video provides behavioural measurements and camera-derived physiological signals that may complement mental health assessment. Their clinical interpretation, however, depends on the acquisition conditions, reference labels, population, and evaluation protocol. This critical narrative review examines facial analysis for mental health screening and symptom estimation, distinguishing depression, anxiety symptoms, experimentally elicited stress, and other condition-specific assessment tasks. We distinguish questionnaire-defined screening labels, symptom severity, experimentally elicited affect or stress, and clinician-established diagnoses. We review image acquisition and preprocessing, facial action units and movement, remote photoplethysmography (rPPG), temporal architectures, and multimodal fusion. Dataset resources are separated by evidential purpose, and representative quantitative results are accompanied by their targets, modalities, and validation conditions rather than presented as a cross-study ranking. The synthesis identifies three recurring limitations: few independent participants despite many video frames, limited validation across populations and recording domains, and a gap between benchmark prediction and clinical utility. Accurate average pulse rate estimation does not establish reliable beat-to-beat variability or disorder-specific validity. We therefore emphasise participant-level evaluation, probability calibration, prevalence-dependent predictive values, acquisition quality, consent, and prospective clinical assessment. Foundation models and longitudinal monitoring remain research directions whose incremental value must be established against appropriate baselines. Current evidence supports investigation of complementary screening and decision support tools, rather than autonomous diagnosis or a demonstrated ability to predict disease onset.