Clustering is a way that may be used to locate groups or clusters within a data collection that is made up of points, patterns, or other objects. The clustering process is a method that can be used to find groups or clusters. Data clustering, which is also known as cluster analysis, is a method that aims to split a data set into clusters of comparable objects based on a similarity measure. The purpose of the data clustering process is to maximise the similarities between the objects that are a part of the same cluster while minimising the similarities between the objects that are a part of separate clusters. Quantifying the degree to which two objects are alike can be done in a variety of different ways. The approach that is most appropriate for your investigation will depend on the kinds of data you have access to as well as the insights you aim to derive from them. Clustering has proven to be useful in many different fields, including but not limited to the following: image segmentation, object and character identification, document retrieval, remote sensing, and data compression, to name just a few of these applications’ possible applications. In this inquiry, we compare and contrast the ability of ELM and No-Prop to classify different kinds of information by using both of their strengths and weaknesses.