Bi‐Model Deep Learning Architectures: A Systematic Review of Fusion Strategies and Design Taxonomies

Bi‐model deep learning, the coordinated use of two complementary models or modalities, has emerged as a powerful paradigm for improving predictive accuracy and robustness in complex real‐world applications. However, existing research remains scattered across domain‐specific studies, with no unified account of the architectural patterns, fusion strategies, and evaluation practices that distinguish bi‐model designs from broader multimodal learning. This systematic review addresses this gap by consolidating 136 publications into a unified taxonomy of parallel, stacked, and interactive bi‐model architectures, together with feature‐level, model‐level, decision‐level, and hybrid fusion strategies. Representative applications across insurance analytics, cybersecurity, medical imaging, emotion recognition, and brain‐computer interfaces are examined to demonstrate how bi‐model systems consistently outperform unimodal baselines when modalities carry complementary information. The review further identifies recurring challenges, including data heterogeneity, representation mismatch, sensitivity to missing modalities, interpretability limitations, and high computation overhead, and proposes future research directions such as adaptive quality‐aware fusion, resilient handling of incomplete modalities, lightweight dual‐encoder designs, and inherently interpretable multimodal reasoning. Beyond synthesizing existing evidence, this review contributes a unified conceptual taxonomy, a consolidated cross‐domain view of practical and real‐time applications, and a structured foundation of open challenges and future directions to guide the design of more accurate, efficient, transparent, and deployable bi‐model systems.