Modern precise clinical decision-making requires visual evidence which shows both body structures as well as functional systems simultaneously. The combination of CT with MRI along with PET and ultrasound imaging and their related variants produces a deeper assessment for diagnostics beyond the capabilities of standalone scans. Recent advancements in machine learning reveal how convolutional and encoder–decoder and transformer systems cooperate to create high-quality multichannel images that benefit cancer margin identification along with brain injury diagnosis and cardiovascular system evaluation and urgent treatment applications. Standardizing input data occurs through critical preprocessing stages which include registration, intensity normalization and noise suppression and these processes simultaneously operate with geometric, elastic and GAN‑based augmentations to handle data scarcity. The results of BraTS and CHAOS benchmark testing along with other benchmarks demonstrate increased success in identifying boundaries together with lesion recognition through evaluation measures IoU, Dice and Hausdorff distance. A comprehensive framework unites CNN, U‑Net, GAN along with Vision Transformer modules through systematic attention protocols and synthetic data creation pathways for achieving secure real-time medical navigation systems as well as precise radiotherapy tools. The benchmarking process demonstrated traditional wavelet methods yield improved Dice scores between 7 and 12% on average but radiologists approved of the system with better lesion boundary detection. Deep-learning-driven fusion builds its core position in next-generation diagnostic imaging based on the combination of survey analysis and proposed innovations.