Multimodal medical image fusion technology optimizes image content by integrating images from diverse modalities, such as Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and Single Photon Emission Computed Tomography (SPECT), while retaining critical information. With the rapid advancements in medical imaging technology, single-modal approaches have limitations in capturing comprehensive anatomical or functional characteristics. As a result, researchers are increasingly turning to multimodal fusion methods to enhance diagnostic accuracy and provide richer data for classification, detection, and segmentation tasks. In particular, during the perioperative period, multimodal image fusion plays a crucial role in surgical planning, intraoperative navigation, and postoperative evaluation, enabling precise localization of  lesions and improving clinical decision-making. This paper presents a survey of the latest literature on medical image fusion, covering three major approaches: traditional methods, model-based methods, and learning-based  methods. It discusses the advantages and limitations of each approach, with a particular emphasis on traditional image processing techniques, model-based fusion methods, and the integration of emerging deep learning (DL) technologies. Comparative experimental analysis highlights performance differences among these methods in terms of information retention, computational efficiency, and clinical applicability. Finally, the paper reviews performance evaluation metrics for multimodal fusion and provides recommendations for future research to further promote the widespread adoption of this technology in clinical diagnostics and intelligent healthcare.