Despite advances in the computational analysis of multiple sclerosis, the ability to accurately predict disease progression remains limited due to unimodal approaches in the context of deep learning. KG-MultiMS proposes as a multimodal deep-learning framework that integrates data from three heterogeneous sources: (1) MRI volumes using the Swin-UNETR architecture to learn both semantic embeddings and segmentation masks of brain lesions; (2) tabular clinical variables using the FT-Tokenizer architecture to learn clinical embeddings; (3) medical knowledge graph based on the Harvard-Oxford atlas using a Heterogeneous Graph Transformer to learn contextual embeddings from patient-specific subgraphs. A cross-attention mechanism will allow for leveraging multimodal contributions by dynamically weighting them based on the anatomical-functional concordance represented in the contextual embeddings.