Image fusion is a technique that combines multiple source images into a single image, capturing additional details and representing information more accurately. This fused image improves precision and provides comprehensive data, beneficial for both human and machine vision in subsequent image processing tasks. In recent years, image fusion has been applied in real-time settings, with spatial domain methods producing high-resolution images. The effectiveness of fusion algorithms depends on the image characteristics and the intended application. In the medical field, multimodal medical image fusion integrates images from various modalities like CT, MRI, PET, and SPECT to enhance diagnostic accuracy. These fused images assist experts in decision-making by preserving essential information from the source images while avoiding distortions. The review explores medical image fusion techniques, particularly wavelet transform, independent component analysis (ICA), and principal component analysis (PCA), which are used for tasks such as denoising and reducing data dimensions, highlighting the potential advantages and challenges of this approach.