Recently, the application of artificial intelligence (AI) algorithms in image processing has become an important field of research, specifically focusing on drone imagery. Thanks to recent advancements in drone technology and their equipment, various types of drones now possess the capability to provide a unique perspective and accurate accessibility for gathering and visualizing data in different environments. To handle and manipulate visual data for diagnostics and biomedical imaging, as well as urban development monitoring and analysis, multiple image processing algorithms have been employed in the field of Artificial Intelligence, intense learning such as computer vision, medical imaging, remote sensing, and multimedia playing a crucial role in tasks ranging from image enhancement and restoration to object recognition and pattern analysis. The purpose of this work is to perform a deep learning-based systematic review of drone-based image processing. It emphasizes methods for contour recognition and image segmentation, with an emphasis on object detection and image analysis across many domains. It also looks at datasets unique to drones, tackling issues brought up by the variety of characteristics found in images taken by drones. By reviewing existing literature and practical solutions, this paper highlights current accomplishments and offers future research paths to build complete image-processing algorithms for drones. In addition to highlighting the value of AI and deep learning in improving drone imagery applications, this systematic review lays the groundwork for further research aimed at removing present obstacles and boosting the potential of drone-based image processing.