Depression is a major mental health challenge, and its early-stage diagnosis is difficult because initial symptoms are often mild, mixed, and not clearly visible. This creates a need for screening methods that can detect early depressive change through more objective indicators. Existing studies have shown that biomarkers such as neural and inflammatory signals, along with behavioral indicators such as speech, facial response, and expressive change, can support depression detection. However, earlier work has examined these signals separately, which limits the ability to capture the full pattern of early-stage depression. This research was therefore needed to address the gap between single-domain models and the clinical need for a more integrated screening framework. In this article, a multimodal methodology is presented that combines biomarker screening, behavioral indicator extraction, feature integration, and early-stage depression classification within one structured framework. The results showed that the integrated model performed better than biomarker-only and behavior-only models, indicating that early depression is better recognized when biological evidence and behavioral signals are examined together. Overall, the study concludes that multimodal screening provides a more complete and clinically useful direction for the early diagnosis of depression.