Nowadays, communication happens anywhere. Communication requires knowledge. In an Internet of Things system, communication of data is dependent on the means of authentication employed by the devices. In this study, we use a variety of machine learning approaches to identify both static and dynamic items, a real-time impediment that is significant in day-to-day living. In this post, we attempted to use a camera to identify numerous challenges in a single picture. The picture data will also be accessible to other network-connected devices. Classifiers such as random forests, decision trees, and the KNN algorithm are utilized for categorizing objects. Determine whether the impediment is dynamic or static employing the classifiers indicated above. Next, evaluate the results; tracking of objects can be used if the impediment is dynamic.