Aim: The aim of this paper is to work on K-means clustering-based radio neutron star pulsar emission mechanism. Background: The pulsars are a rare type of neutron stars that produce radio rays. Such types of rays are detectable on earth and have attracted scientists because of their concern with space-time, interstellar medium, and states of matter. During the rotation of pulsar rays, rays are emitted in the whole sky and after crossing the threshold value, the pattern of radio emission broadband is detected. As rotation speed of pulsar increases then accordingly the types of the patterns are produced periodically. Every pulsar emits different patterns that are a little bit different from each other which fully depends on its rotation. The detected signals are known as a candidates. It can be determined by the length of observation and it is average of all the rotations of the pulsar. Objective: The main objectives of this radio neutron star pulsar emission mechanism are: (a) Decision Tree Classifier (2) K-means Clustering (3) Neural Networks. Methods: The Pulsar Emission Data was broken down into two sets of data: Training Data and Testing Data. The Training Data is used to train the Decision Tree, the algorithm, K-means clustering and Neural Networks to allow it to identify, which attributes (Training Labels) are useful for identification of Neutron Pulsar Emissions. Results: The analysis used multiple machine learning algorithms. It was concluded that using neural networks is the best possible method to detect pulsar emissions from neutron stars. The best result achieved was 98% using Neural Networks. Conclusion: There are so many benefits of pulsar rays in different technologies. Earth can detect pulsar ray from low orbit. Earth can completely absorb X-ray in the atmosphere and due to this reason we can say that the wavelength is limited to those who do not have an atmosphere like space. According to our result, it can be concluded that the algorithm can be successfully used for detecting the pulsar signals. © 2021 Bentham Science Publishers.