The field of machine learning is witnessing a significant shift towards multimodal learning, where multiple sources of data are integrated to improve prediction and clustering performance. This trend is evident in the development of novel deep learning architectures that can effectively combine different modalities, such as retinal images and clinical data, to detect stroke and predict future risk. Another area of focus is on clustering algorithms, where researchers are exploring new approaches to improve the accuracy and robustness of clustering in non-Euclidean spaces. Notable papers in this regard include the proposal of a multimodal deep neural network for stroke detection and risk prediction, which achieved a 5% AUROC improvement over unimodal image-only baselines, and the development of a Conjoint Graph Representation Learning framework for hypertension comorbidity risk prediction, which provided more accurate predictions than other strong models. Additionally, the introduction of Hyperbolic Fuzzy C-Means with Adaptive Weight-based Filtering and Iterative Adaptive Resonance Theory demonstrate the growing interest in clustering algorithms that can handle complex, high-dimensional, and non-Euclidean datasets.