Image fusion involves the integration of various medical images obtained through distinct imaging modalities, such as CT and MRI, to produce a composite image that more accurately represents the pathological condition. This chapter examines the progression of image fusion, tracing its development from rudimentary additive image combinations to sophisticated artificial intelligence methodologies, including deep learning and generative adversarial networks (GANs). Techniques such as real-time fusion systems, federated learning, and others are employed to mitigate issues related to noise, misregistration, and data heterogeneity. The incorporation of multimodal imaging is particularly beneficial in fields such as neurology, cardiology, oncology, and various other medical specialties. The future trajectory of image fusion is oriented toward augmented reality, personalized medicine, and quantum computing, in line with current trends. It is imperative to ensure the ethical utilization of data and to implement measures to prevent bias. Ongoing research endeavors are refining these technologies, fostering academic collaboration to enhance clinical outcomes and patient care on a global scale.