Embedded systems have become an essential backbone of modern digital applications, with real-time image processing emerging as one of their most impactful domains. The increasing demand for intelligent vision-based solutions in areas such as healthcare, automotive safety, industrial automation, and security surveillance has accelerated research on resource-efficient embedded architectures. Unlike conventional computing platforms, embedded systems impose stringent constraints on processing power, memory capacity, and energy consumption. This paper presents a detailed study on the design considerations, computational models, and optimization strategies that enable efficient real-time image processing in embedded environments. By integrating hardware accelerators, optimized algorithms, and specialized system-on-chip (SoC) architectures, embedded systems can achieve low-latency performance while maintaining portability and cost-effectiveness. The work also examines the challenges associated with handling high-resolution image streams and the role of parallelism and deep learning integration in overcoming these barriers