Research progress in multi-modal information fusion, particularly in Image Fusion, has experienced significant advancements over the last decade. By integrating information from multiple sources or modalities, image fusion enables the extraction of comprehensive insights and facilitates more accurate analysis and decision-making processes. The inherent complexity of image fusion, stemming from its unstructured nature, necessitates high levels of abstraction and intricate data representation. The utilization of deep learning, notably CNN and more recently introduced Vision Transformer, has yielded substantial enhancements in image fusion methodologies. This paper presents a comprehensive survey of image fusion methodologies, focusing on recent advancements and introducing a novel taxonomy based on supervised, unsupervised, and task-driven approaches. The survey encompasses recent contributions, including the integration of transformer architectures, which have emerged as powerful tools for image fusion tasks. This classification is supported by a distinction of methods by architecture type (CNN, GAN, Transformer) for a better understanding of the relationships between methods. Through the synthesis of existing literature and the introduction of a new classification paradigm, this survey aims to provide researchers and practitioners with a comprehensive overview of image fusion techniques and guide future research directions in this rapidly evolving field.