Malicious URL detection plays a critical role in online security by detecting threats such as phishing, malware, and spam. This study compares manual and automated approaches to identify malicious URLs using various databases from repositories such as PhishTank and URLhaus. The manual process generated 47 features based on domain expertise, while automated tools such as fresh and Feature tools generated over 600 features based on descriptive, receptive, and behavioral features. Seven machine learning algorithms, including random forest, gradient boosting, and support vector machines, are evaluated using metrics such as precision, accuracy, recall, and F1 score. The results show that automatic feature detection improves on manual methods by 3–5% accuracy. Gradient boosting and random forest are the best classification algorithms with automatic features that help in efficiently detecting complex patterns. This study highlights the advantages of automation and description and provides valuable insights for developing robust and weak URL detection systems. Future work will explore the combination of machine learning and predictive AI with real-time detection capabilities to improve security.