Multimodal AI technologies are transforming medical practices by integrating different data sources for more accurate diagnosis, disease prediction, and treatment planning. The review examines state-of-the-art multimodal AI systems in the clinical setting, including radiology, pathology, clinical imaging, electronic health records, and multiomic data. The combination of multiple modalities improves diagnostic accuracy and prognostic prediction compared to unimodal models. Emphasis is placed on robust data fusion strategies and interpretability of models for real-world clinical deployment. It addresses key challenges such as data heterogeneity and uncertainty quantification. The research offers a new paradigm for intelligent healthcare. The findings suggest that advances in multimodal AI will improve clinical decision-making, pave the way for personalized medicine, and improve patient outcomes.