Due to their covert nature and connections to illegal activity, darknet network traffic presents serious difficulties for safety and surveillance. This paper investigates the use of machine learning techniques in identifying types of traffic on darknet networks as a means of addressing these issues. Network traffic recorded from darknet environments is gathered into a labeled dataset, which is then extracted to extract pertinent information. The efficacy of several machine learning techniques, such as decision trees, random forests, AdaBoost, KNN, Naïve Bayes, simple CART, and gradient boosting, in categorizing darknet traffic is assessed. The training set of the dataset is used to train the chosen machine learning models. The dataset is split into sets for testing and training. The models first identify deeper patterns and connections among the characteristics of network traffic before classifying the data appropriately. The models’ ability to classify is then assessed using the testing set to determine the F1-score, accuracy, precision, and recall. The outcomes show how well machine learning algorithms categorize traffic from multilayered darknet networks. The chosen models identify different kinds of malevolent or unlawful activity happening on the darknet with excellent accuracy and intriguing possibilities. The study also addresses how the models can be interpreted and what can be learned from the significance of features analysis, which helps to clarify the functioning of darknet networks.