Detecting depression from user-generated posts on social media platforms offers significant potential for early intervention on at-risk individuals. Existing works mainly concentrate on text processing, and only a limited number incorporate images posted by users. These image-integrated methods face challenges in modeling the intricate relationships between textual and visual features. Besides, the absence of approaches that explore psychological trajectory of users by analyzing their posts over time leaves a critical gap in capturing the progression of depressive symptoms. In this paper, we propose A Time-Aware Mental State Space (T-M2S) for detecting depression from social media posts. We introduce a Cross-Modal Learning that effectively integrates text and image embeddings into sentiment-oriented unified representations. Additionally, we design a Mental State Space to analyze users’ posts over time, offering a nuanced understanding of emotional dynamics. Extensive experiments on Twitter and Reddit datasets demonstrate that T-M2S significantly outperforms state-of-the-art methods. Code and models are available at GitHub.