In the field of medical AI, feature fusion is the process of combining data from several sources to improve therapy effectiveness and diagnostic precision. To produce a comprehensive picture of patient health, this method integrates multiple data sources, including laboratory findings, electronic health records, and medical imaging. Feature fusion models can capture intricate relationships and patterns in a variety of datasets by utilizing various machine learning methods. Better clinical results, individualized treatment regimens, and more accurate disease detection are made possible by these combined insights. While there are still obstacles to overcome, such as privacy issues and data standardization, feature fusion is a promising field in medical AI research because it holds the promise of more all-encompassing, data-driven healthcare solutions. Based on the commonly utilized image fusion techniques, imaging modalities, and imaging of the organs being studied, we describe the medical image fusion research. Despite some unresolved scientific and technological challenges, the fusion of medical images has proved useful in enhancing the clinical dependability of employing medical imaging for medical diagnosis and analysis. This review discusses the medical image fusion, a scientific field that has the potential to grow significantly in the years to come.