Computer Vision (CV) and Image Processing (IP) have emerged as fundamental pillars in modern computational intelligence, enabling machines to interpret, analyze, and make decisions based on visual data. Over the past decades, these domains have undergone a significant transformation from traditional algorithmic approaches to data-driven deep learning paradigms. This review provides an extensive overview of classical image processing techniques, feature extraction methods, segmentation strategies, and modern deep learning-based frameworks such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Generative Adversarial Networks (GANs). Furthermore, the chapter explores real-world applications across diverse sectors including healthcare, autonomous systems, surveillance, and smart industries. Key challenges such as data dependency, computational complexity, and model interpretability are critically analyzed.