Many areas, including security tracking systems, have been completely changed by the fast progress made in machine learning (ML). Smart machine learning methods are being added to picture and video analysis, which is changing how security operations are done by making it easier to watch in real time, find problems, and evaluate threats.  This essay looks into how machine learning algorithms, especially deep learning models, could be used to make tracking systems faster, more accurate, and better able to respond. The main goal is to look into how advanced machine learning methods can be used to improve security camera images and videos by focusing on things like finding objects, recognizing activities, recognizing faces, and analyzing behaviour. The study focusses on how to use reinforcement learning (RL), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) to track and find objects in real time. There is also work being done on creating algorithms that can spot shady behaviour, making face recognition systems better, and making video analytics work better.  The paper also talks about how these smart machine learning models could be added to cloud-based monitoring systems to help them be more flexible and give law enforcement agencies access from anywhere.  It is also talked about in length how these technologies can help cut down on false alarms, find small trends, and give security staff automated tools for making decisions.