This paper introduces a novel semi-supervised neuro-fuzzy system to predict ICU admissions among post-COVID organ transplant recipients. Addressing the challenges of small sample sizes and lacking labels in organ transplantation, our study takes on these issues by proposing a DBN-Based Dual Manifold Regularized Fuzzy Broad Learning System (D-DMR-FBLS). This system utilizes the streamlined and flat architecture of the Broad Learning System (BLS), integrating Deep Belief Networks (DBN) and Takagi-Sugeno-Kang (TSK) systems to enhance representation learning capacities during the Unsupervised Training Phase (UTP). The system combines the strong feature learning capabilities of DBN with the powerful fuzzy rule extraction capacity of the TSK system, enhancing the model’s predictive performance and generalization capability. Moreover, we propose two types of graph-based manifold regularization, sample-based and feature-based, within this novel D-DMR-FBLS framework. Our method enhances its predictive ability by exploiting both the similarity among unlabeled and labeled patient samples, as well as the correlations between features within the fuzzy feature space. Employed to predict ICU admission risks in post-transplant COVID-19 patients, the method has demonstrated superior performance over existing methods, particularly in scenarios with limited samples and labels, thereby providing more accurate decision support for medical professionals in optimizing resource allocation for transplant patients.