Image fusion in remote sensing has emerged as a sought-after protocol because it has proven beneficial in many areas, especially in studies of agriculture, environment, and related fields. Simply put, image fusion involves garnering all pivotal data from many images and then merging them in fewer images, ideally into a solitary image. This is because this one fused image packs all the pertinent information and is more correct than any picture extracted from one solitary source. It also includes all the data that is required. Additional advantages are: it lessens the amount of data and it creates images that are appropriate and that can be understood by humans and machines. This paper reviews the three image fusing processing levels, which include feature level, decision level, and pixel level. This paper also dwells upon image fusion methods that fall under four classes: MRA, CS, model-based solutions and hybrid and shows how each class has some distinct advantages as well as drawbacks.