Depression remains one of the most serious mental health challenges worldwide and is often underdiagnosed due to social stigma and limited access to medical services. With the proliferation of social media as a medium for self-expression, these platforms provide new opportunities for early detection of depressive symptoms through digital footprints. However, most prior research has primarily focused on linguistic features derived from text, overlooking behavioral dynamics that also reflect psychological states. To address this gap, we propose Depress-HybridNet, a hybrid deep learning framework that integrates linguistic embeddings with behavioral activity patterns. The architecture combines a BERT-BiLSTM encoder for linguistic feature extraction, a multi-layer perceptron for behavioral feature encoding, and an adaptive attention-based fusion mechanism to integrate multimodal signals optimally. Experiments conducted on the publicly available Kaggle Depression Dataset demonstrate that Depress-HybridNet consistently outperforms strong baselines, including fine-tuned BERT, achieving state-of-the-art results (F1 = 0.92, AUC = 0.93). A further ablation study highlights the critical role of behavioral features and the attention fusion layer in improving performance. In addition, clinical validation by licensed psychologists confirmed a high degree of consistency between the model’s predictions and expert judgment, underscoring its real-world applicability. These findings underscore the importance of modelling depression as a multifaceted phenomenon, rather than a purely linguistic task, and establish a reproducible benchmark for future research.