Today, technology is undergoing a rapid evolution, with computer science at the core of this transformation. In particular, the disciplines of artificial intelligence (AI) and image processing have gained increasing importance in both academic research and industrial applications. Image processing is defined as the process of acquiring an image and applying digital, optical, or analog techniques to either obtain an enhanced image or extract various features and information from it. AI, on the other hand, is a technology that aims to develop computer systems capable of human-like learning, reasoning, and problem-solving.

AI-supported image processing systems offer higher accuracy and processing speed compared to traditional methods, playing a critical role in a wide range of applications, including object recognition, medical image analysis, security systems, and space research. This study examines the conceptual framework and historical development of AI and image processing, followed by a discussion of their applications in healthcare, transportation, security, and space. Examples include the Google LYNA algorithm for cancer cell detection, SkinVision for skin lesion analysis, Tesla’s autonomous driving system, Apple Face ID, and the Surya model for predicting solar activity. The literature review evaluates the effectiveness of deep learning–based methods through algorithms such as YOLO, R-CNN variants, and SSD, and the study aims to elucidate the technical foundations of AI-supported image processing methods while demonstrating how theoretical knowledge is translated into practical applications.