Identification of wormhole attacks is essential in WSNs as such networks can easily fall prey to various security threats. Wormhole attacks are particularly threatening due to the fact that it opens other unauthorized channels between distant nodes, which in turn brings about the insecurity and instability of the network function. The prevention of such schemes is important in order to provide integrity and accessibility of WSNs which are widely used in the current society for purposes of assessing environments, surveillance security, and automation industries. In this paper, we consider a new and efficient approach using Random Forest (RF) algorithm in order to detect wormhole attacks in WSNs. The RF method is chosen due to its superior performance in terms of complexity of the classification process in the present study. Random forest is an instance of ensemble learning where many decision trees improve the classification accuracy of the network, and also deal with the inherent noise in the WSN data. All the decision trees imposed in the forest help come up with the final decision, and thus making the performance of the model more powerful by voting. This combines the merits of decision tree classifiers but does not inherit all the demerits like overfitting and vulnerability to noise of these classifiers. The RF model of the proposed system is trained with normal and attack traffic samples which allow the model to learn such characteristics as big differences in the traffic pattern and other wormhole attack features. The effectiveness of the model is, therefore, determined by measures such as accuracy, precision, recall, and F1-Score as well as False Positive Rate. The results clearly show that the RF-based method is exceptional in terms of accurately identifying wormhole attacks. It indicates that the efficacy of participants for recall is as accurate for precision, and general false positive rate shows that a low number of patients are misidentified from the negative class.