We present **Dash-M5H**, an interactive dashboard for *multi-modal, multi-model mental health* assessment that helps clinicians and researchers jointly inspect multimodal behavioral data with multi-model signal outputs of recorded clinical interviews. Guided by signal detection and integrated sensemaking theories, Dash-M5H synchronizes transcript text, audio, and facial behavior (action units and gaze) to support overview-to-detail evidence tracing; and it integrates extracted signals (e.g., sentiment and facial activity) with a clinically grounded VLM prediction pipeline that produces DSM-5-aligned depression predictions. Dash-M5H (https://dash-m5h.io) is implemented in a lightweight, browser-based stack (Quarto + Observable JS + D3), supports local data import and time-synced clinical annotation with export. We demonstrate Dash-M5H through a depression screening scenario, evaluate its note-taking and screening capabilities through a user experiment, and release a live demo (https://youtu.be/w3qCJ02k6bw) and code (https://github.com/nd-hal/M5H-Dashboard-VLM) to facilitate reproducible evaluation.