In the fields of Computer Vision, Image Semantic Segmentation is one of the most focused research areas. These are widely used for several real-time problems for finding the foreground or background scenes of a given image or a video.  Initially, it is achieved using computer vision techniques, later once the deep learning is in its rise, ultimately it took over the entire image classification and segmentation techniques. These are widely surveyed and reviewed as they are used in several Image Processing, Feature Detection and Medical Fields. All the models for implementing Image Segmentation are mostly done using a specific neural network architecture called a convolution neural network. In this work, firstly we'll study the implementation of Image Segmentation models and advantages, disadvantages over one another including their development trends. We'll be discussing all the models and their applications concerning other fancy methods that are mostly used which involves hyperparameters and the transitive comparison between them.