The exploration of deep space has always been a source of fascination for humans, with astronomical photography providing crucial insights into the enormous expanse of the cosmos. The study explores artificial intelligence's transformational role in furthering astronomical science. It begins by emphasizing the importance and problems of recognising celestial objects in complicated and massive astronomical data. The basic ideas of machine learning and artificial intelligence (AI) are then briefly discussed, emphasizing how they are used in object detection and picture processing. The key preprocessing approaches for improving the quality and usability of astronomical photographs are thoroughly explored. The work examines different AI approaches, including Convolutional Neural Networks (CNNs), Transfer Learning, and advanced models like YOLO and Mask R-CNN, to demonstrate their usefulness in finding and categorizing celestial events. Real-world case studies demonstrate how these techniques can be used to detect exoplanets, galaxies, and supernovae and contribute to gravitational wave research and radio astronomy. Looking ahead, the chapter discusses potential directions, including advances in AI algorithms, integration with robotic telescopes, ethical implications, and obstacles like data constraints and bias. This chapter hopes to inspire further advances in the quest to understand the universe by bridging the gap between cutting-edge AI technology and astronomical exploration.