This chapter examines the utilization of deep learning methodologies in the domain of medical image fusion with a focus on innovative techniques for the integration of multimodal data. It underscores the significance of convolutional neural networks (CNNs) and generative adversarial networks (GANs) as principal frameworks for feature extraction, similarity processing, and image reconstruction. Advanced models, such as DenseNet-based architectures and dual-discriminator conditional GANs (DDcGAN), exhibit superior performance in preserving structural details and enhancing diagnostic quality. The chapter further explores encoder-decoder architectures, wherein dense blocks facilitate the extraction of multiscale features, and perceptual loss functions ensure the production of high-quality fused outputs. Additionally, unsupervised deep learning frameworks address challenges such as limited labeled datasets, while adversarial training enhances the realism of fusion. These AI-driven approaches are crucial in amalgamating complementary information from modalities such as MRI, CT, and PET, thereby enabling precise clinical decision-making. By addressing limitations such as vanishing gradients and ensuring robust feature fusion, these methods pave the way for scalable, automated solutions in medical imaging. The chapter highlights the transformative potential of deep learning in advancing image fusion for improved healthcare outcomes.