With the quick advancement of wireless communication technologies and the rising demand for spectrum resources, integrating learning and reasoning capabilities into cognitive radio networks (CRN) has become essential. This study explores the spectrum sensing capabilities of Secondary Users (SUs) within a CRN. Various supervised machine learning (ML) techniques, including Bayes Network, Naive Bayes, and Decision Tree, are employed to assess the performance of the Secondary Users in spectrum sensing. These algorithms are used in order to predict the trustworthiness of Secondary Users by considering sensing reputation values altogether. It is a topic of discussion, to identify the most effective ML algorithm that can guarantee the maximum level of system accuracy & efficiency. In this study, the open-source data mining tool WEKA is used to create a wide range of classification models for the evaluation of performance and correct prediction of trustworthy users. The best possible model accuracy is attained with the aid of Receiver Operating Characteristics (ROC) curves and cost-benefit analysis of three different classifiers. The evaluation of SUs’ trustworthiness and spectrum sensing repute in CRN concludes that the Decision Tree classifier provides best performance which enables the system for correct classification among malevolent users, suspicious users, and honest users.