This article provides a comprehensive survey of medical image fusion, a process that combines multiple images from different modalities to enhance diagnostic accuracy and reduce redundancy. The authors discuss the benefits of multi-modal fusion, including improved clinical decision-making and robust information processing. They categorize the research based on image fusion methods, imaging modalities, and organs studied. The review highlights the challenges in image registration, feature selection, and the impact of noise and variability. It also explores various fusion techniques, such as morphological, knowledge-based, wavelet-based, neural network-based, and fuzzy logic-based methods. The article further details the application of these techniques in specific imaging modalities like MRI, CT, PET, SPECT, and ultrasound, and their use in diagnosing and treating conditions in organs such as the brain, breast, prostate, lungs, and liver. The authors conclude that while there are technological and scientific challenges, medical image fusion has significant potential for advancing clinical reliability and is expected to grow in the coming years.This article provides a comprehensive survey of medical image fusion, a process that combines multiple images from different modalities to enhance diagnostic accuracy and reduce redundancy. The authors discuss the benefits of multi-modal fusion, including improved clinical decision-making and robust information processing. They categorize the research based on image fusion methods, imaging modalities, and organs studied. The review highlights the challenges in image registration, feature selection, and the impact of noise and variability. It also explores various fusion techniques, such as morphological, knowledge-based, wavelet-based, neural network-based, and fuzzy logic-based methods. The article further details the application of these techniques in specific imaging modalities like MRI, CT, PET, SPECT, and ultrasound, and their use in diagnosing and treating conditions in organs such as the brain, breast, prostate, lungs, and liver. The authors conclude that while there are technological and scientific challenges, medical image fusion has significant potential for advancing clinical reliability and is expected to grow in the coming years.