Object Detection and the Object Pursuit are the fundamental and the emerging tasks in the Machine learning and in the computer vision to detect the object and then too track the object in all the real and the dynamic environments. The latest trends which are emerged in this area, highlighting the embedding of deep learning techniques has transformed the field of object detection and tracking. Methods like Convolutional Neural Networks, Deep SORT, You Only Look Once and Region-Based Convolutional Neural Networks have significantly improved accuracy and efficiency. We examine the shift towards more robust and measurable and the scalable solutions, with particular focus on multi-object tracking, real-time processing, and handling challenging Challenges like occlusion, variations in scale, and varying in illumination. The survey also addresses key challenges that remain, including computational efficiency, accuracy in complex scenarios, and the development of algorithms. Furthermore, we discuss the applications of object detection and pursuit across industries like autonomous driving, robotics, surveillance, and augmented reality, while offering insights into future research directions that may overcome existing limitations and drive the field forward. These recent advancements, combined with the evolution of tracking algorithms, have made it possible to detect and track objects in real-time with high precision.