Recent years have witnessed unprecedented advancements in deep learning, particularly in the domain of real-world object detection. The sophisticated capability of contemporary deep learning methodologies to extract and process features from intricate data has catalyzed their adoption across diverse fields, including computer vision, robotics, and autonomous systems. This comprehensive literature survey examines cutting-edge deep learning approaches employed for effective real-world object detection. The review encompasses various dimensions of object identification, such as detection, recognition, and segmentation, while delving into diverse deep learning frameworks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Belief Networks (DBNs). Furthermore, the survey offers a thorough examination of current research developments, addressing critical challenges and constraints, while identifying promising avenues for future research endeavors. The review delivers valuable insights into state-of-the-art techniques and their potential real-world applications.