— Image fusion is a critical aspect of various fields, including medical imaging, remote sensing, surveillance, and computer vision. Among the different fusion techniques available, Morphological Component Analysis (MCA) has emerged as a powerful approach for effectively combining multiple images. By leveraging mathematical morphology and signal processing, MCA enables the extraction and integration of relevant information from input images. This research paper aims to provide a comprehensive review of image fusion techniques based on Morphological Component Analysis. It begins with an overview of MCA, delving into its underlying principles and exploring its applications across different domains. The paper further investigates various MCAbased fusion algorithms, discussing their advantages and limitations. Additionally, it highlights recent advancements in the field, addresses existing challenges, and identifies potential avenues for future research in the realm of image fusion utilizing Morphological Component Analysis.