This paper proposes the implementation of a system for processing satellite images using parallel programming platforms such as CUDA and OpenCL. In areas like image processing, computer vision, and big data analytics, the volume of multimedia data is growing, which has made it difficult to analyze and use information efficiently. Traditional methods for processing such data need a lot of time and resources. The proposed system seeks to identify significant changes in environmental parameters using various image processing techniques while allowing hardware and operating system independence. This paper includes a performance benchmarking analysis between traditional and parallel implementations. The proposed system has the potential to be time and energy efficient while giving scientists, planners, and decision-makers access to a variety of valuable data for effective policy and decision-making. Change detection in satellite images is a key application area of the proposed system.