Medical image fusion combines multiple imaging modalities to enhance diagnostic accuracy and clinical applicability. This review summarizes current methods, challenges, and applications in medical image fusion. Key methods include morphological, knowledge-based, wavelet-based, neural network-based, fuzzy logic-based, and other techniques. These methods aim to improve image quality, detect abnormalities, and support clinical decision-making. Common imaging modalities used in fusion include MRI, CT, PET, SPECT, ultrasound, and others. Applications span various organs, such as the brain, breast, prostate, lungs, and liver, with fusion techniques aiding in diagnosis, treatment planning, and surgical guidance. Challenges include technical limitations, image variability, and the need for accurate registration and feature extraction. Despite these challenges, medical image fusion has shown promise in improving diagnostic accuracy and clinical outcomes. Future research should focus on advancing fusion techniques, improving algorithm robustness, and enhancing the usability of multimodal systems in clinical settings. The field is expected to grow significantly with continued technological advancements and increased clinical adoption.Medical image fusion combines multiple imaging modalities to enhance diagnostic accuracy and clinical applicability. This review summarizes current methods, challenges, and applications in medical image fusion. Key methods include morphological, knowledge-based, wavelet-based, neural network-based, fuzzy logic-based, and other techniques. These methods aim to improve image quality, detect abnormalities, and support clinical decision-making. Common imaging modalities used in fusion include MRI, CT, PET, SPECT, ultrasound, and others. Applications span various organs, such as the brain, breast, prostate, lungs, and liver, with fusion techniques aiding in diagnosis, treatment planning, and surgical guidance. Challenges include technical limitations, image variability, and the need for accurate registration and feature extraction. Despite these challenges, medical image fusion has shown promise in improving diagnostic accuracy and clinical outcomes. Future research should focus on advancing fusion techniques, improving algorithm robustness, and enhancing the usability of multimodal systems in clinical settings. The field is expected to grow significantly with continued technological advancements and increased clinical adoption.