Deep learning models often struggle to maintain robust generalizability in medical imaging, particularly under domain-fracture scenarios where distributional shifts arise due to varying imaging techniques, acquisition protocols, patient populations, demographics, and equipment. In practice, each hospital may need to develop and train distinct models—differing in functionality (i.e., learning task) and morphology including width and depth—to handle their local data distributions. For example, while one hospital may utilize Euclidean architectures such as MLPs and CNNs to process structured tabular data or regular grid-like image data, another hospital may need to deploy non-Euclidean architectures such as graph neural networks (GNNs) to process inherently irregular data like brain connectomes or other graph-structured biomedical information. However, how to train such heterogeneous models coherently across different datasets, in a manner that enhances the generalizability of each model, remains an open and challenging problem. In this paper, we address this issue by introducing a new learning paradigm, namely unified learning. To address the topological differences between these heterogeneous architectures, we first encode each model into a graph representation, enabling us to unify these diverse models within a shared graph learning space. Once represented in this space, a GNN guides the optimization of the unified models. By decoupling the parameters of individual deep learning models and controlling them through the unified GNN (uGNN), our approach enables parameter-sharing and knowledge-transfer across varying architectures (MLPs, CNNs and GNNs) and distributions, ultimately improving its generalizability. We evaluate our framework on MorphoMNIST and two MedMNIST benchmarks—PneumoniaMNIST and BreastMNIST—and find that our unified learning improves the performance of individual models when trained on unique distributions and tested on mixed ones, thereby demonstrating generalizability to unseen data with strong distributional shifts. Our source code including benchmarks and evaluation datasets is available at https://github.com/basiralab/uGNN .