Depression and academic stress undermine students' attendance, performance, and retention, yet educational institutions lack reliable early-alert tools. Because about three-quarters of lifetime psychiatric conditions emerge before age 25, timely automated screening is critical for early intervention. This paper proposes a multi-source ensemble learning framework for distinguishing {\it depression} (DP) from {\it non-depression} (ND) using 1,547 harmonized samples drawn from six real-world sources: occupational surveys, student academic stress records, Twitter sentiment data, Reddit depression posts, fine-grained emotion corpora, and behavioural monitoring signals. Academic stress and behavio... More >