This paper explores the detection of fraudulent URLs that lead to malicious websites, a critical form of defense against phishing attacks. The study focuses on the application of machine learning (ML) and quantum machine learning (QML) techniques to accurately identify these URLs. The research begins with an essential data preparation phase, evaluating several traditional ML models using different datasets and achieving true positive rates over 90%. The study then moves to the application of QML, analyzing its specificities and assessing its potential for detecting malicious URLs. Given the limited literature on QML for cybersecurity, this work represents a significant novelty. Encouraging results from QML algorithms, such as the Variational Quantum Classifier (VQC), suggest further research into the integration of quantum computing in cybersecurity. The paper concludes by highlighting the importance of optimizing QML parameters and integrating these algorithms into cybersecurity solutions for early detection of fraudulent activities.This paper explores the detection of fraudulent URLs that lead to malicious websites, a critical form of defense against phishing attacks. The study focuses on the application of machine learning (ML) and quantum machine learning (QML) techniques to accurately identify these URLs. The research begins with an essential data preparation phase, evaluating several traditional ML models using different datasets and achieving true positive rates over 90%. The study then moves to the application of QML, analyzing its specificities and assessing its potential for detecting malicious URLs. Given the limited literature on QML for cybersecurity, this work represents a significant novelty. Encouraging results from QML algorithms, such as the Variational Quantum Classifier (VQC), suggest further research into the integration of quantum computing in cybersecurity. The paper concludes by highlighting the importance of optimizing QML parameters and integrating these algorithms into cybersecurity solutions for early detection of fraudulent activities.