This paper provides a comprehensive review of deep learning techniques applied to semantic segmentation. Semantic segmentation is a key problem in computer vision, essential for applications such as autonomous driving, indoor navigation, and augmented reality. The paper discusses the terminology, background concepts, datasets, challenges, and existing methods in semantic segmentation. It highlights the importance of deep learning in achieving accurate and efficient segmentation, with Convolutional Neural Networks (CNNs) being the primary approach. The paper reviews various deep learning architectures, including AlexNet, VGG, GoogLeNet, ResNet, and ReNet, and discusses their contributions to the field. It also addresses transfer learning, data preprocessing, and augmentation techniques that are crucial for effective segmentation. The paper presents a detailed analysis of popular datasets such as PASCAL VOC, COCO, Cityscapes, and KITTI, which are widely used in semantic segmentation research. It also discusses 2.5D and 3D datasets that include depth information. The paper reviews various methods for semantic segmentation, including FCN, SegNet, and methods that integrate context knowledge using Conditional Random Fields (CRFs) and dilated convolutions. The paper concludes with a discussion of the current state of the art in semantic segmentation using deep learning techniques and identifies promising future research directions.This paper provides a comprehensive review of deep learning techniques applied to semantic segmentation. Semantic segmentation is a key problem in computer vision, essential for applications such as autonomous driving, indoor navigation, and augmented reality. The paper discusses the terminology, background concepts, datasets, challenges, and existing methods in semantic segmentation. It highlights the importance of deep learning in achieving accurate and efficient segmentation, with Convolutional Neural Networks (CNNs) being the primary approach. The paper reviews various deep learning architectures, including AlexNet, VGG, GoogLeNet, ResNet, and ReNet, and discusses their contributions to the field. It also addresses transfer learning, data preprocessing, and augmentation techniques that are crucial for effective segmentation. The paper presents a detailed analysis of popular datasets such as PASCAL VOC, COCO, Cityscapes, and KITTI, which are widely used in semantic segmentation research. It also discusses 2.5D and 3D datasets that include depth information. The paper reviews various methods for semantic segmentation, including FCN, SegNet, and methods that integrate context knowledge using Conditional Random Fields (CRFs) and dilated convolutions. The paper concludes with a discussion of the current state of the art in semantic segmentation using deep learning techniques and identifies promising future research directions.