Effective detection of depression is crucial for early identification of potential depression risks. Traditional methods rely on clinical interviews， which are highly subjective and prone to misdiagnosis and delayed treatment. With the development of artificial intelligence， Deep Learning （DL） technology， with its superior feature representation and modeling capabilities， is becoming a core method for depression detection gradually. However， most existing reviews still focus on single-modality or traditional methods， lacking a systematic summary of multimodal DL methods in the field of depression detection， while multimodal data has significant advantages in providing complementary clues for depression. Therefore， typical DL models and multimodal fusion strategies were reviewed systematically， and the core techniques and latest advancements in this field were summarized. First， this paper reviewed the evolution of depression detection methods from single-modality to multimodality， and formed a development trajectory based on datasets and methods. Second， it focused on discussing popular DL models and multimodal fusion strategies in depression detection applications， compared the performance differences of different models and fusion strategies， and reviewed the advantages and limitations of different multimodal fusion techniques. Finally， it summarized the challenges and potential directions for future innovation in this field.