<p>Advancing intelligent aquaculture relies on effectively integrating complex heterogeneous data. Deep learning–enhanced multimodal fusion has emerged as a transformative approach for extracting complementary features from visual, acoustic, textual, and other heterogeneous modalities. These approaches demonstrate significant advantages over single-modality methods. In contrast to previous reviews focusing on single modalities or isolated applications, we provide a comprehensive review of deep multimodal fusion methods and their applications in aquaculture. We systematically categorize current fusion approaches into five major categories: encoder-decoder methods, attention-based fusion, graph neural network–based fusion, generative neural network–based fusion, and coordination-based fusion. The review examines their architectural principles, technical advantages, and implementation challenges. Furthermore, practical application analysis in aquaculture covers three critical domains: water quality monitoring and prediction, biomass estimation, and fish behavior analysis. We identify current research obstacles, including data heterogeneity, missing modality handling, real-time processing, and the absence of domain-specific large models. To address these challenges, we explore emerging technologies such as adaptive fusion strategies, edge-cloud architectures, and transfer learning, which can improve system accuracy, robustness, and deployment feasibility. This work provides a roadmap for advancing next-generation intelligent aquaculture systems.</p>