Classifying URLs is crucial for enhancing network security by identifying and blocking harmful websites. This research contrasts the effectiveness of traditional machine learning models, a basic convolutional neural network (CNN), and a quantum convolutional neural network (QCNN) in URL categorization. QCNN leverages quantum mechanics to enhance computational efficiency. QCNN transforms classical data into quantum states using amplitude encoding and employs quantum-specific convolutional and pooling layers to extract features. The traditional machine learning (ML) models examined are Decision Tree, Random Forest, AdaBoost, K-Nearest Neighbors, Stochastic Gradient Descent, Extra Trees, and Gaussian Naïve Bayes. We assessed the performance of these models using accuracy, precision, recall, and F1 score measures. Findings suggest that the QCNN surpasses other models in all these metrics. While the basic CNN and grouped methods like Random Forest and Extra Trees also exhibit high performance, the QCNN’s exceptional results stem from its capacity to recognize intricate data patterns through quantum mechanics, enhancing its classification capabilities. Hence, the QCNN emerges as a leading choice for practical uses in flagging harmful URLs, with the basic CNN and grouped methods as effective alternatives.