In recent years, advances in machine learning and computer vision have driven continual learning (CL), allowing models to learn new tasks incrementally while retaining prior knowledge without full retraining. Early CL focused on unimodal data like images for classification, but powerful multimodal models now unify images, videos, text, and audio. Multimodal continual learning (MCL) must tackle unique challenges, including modality-specific forgetting, imbalance, and maintaining cross-modal links. This MCL workshop will address these issues, highlight new research directions, and promote collaboration among researchers, practitioners, and industry, advancing inclusive, efficient continual learning for modern AI systems.