This research presents a comparative study of machine learning and deep learning techniques for detecting malicious URLs, a key concern in cybersecurity. While machine learning methods are widely used, limited work has explored the effectiveness of stacked models combining multiple algorithms. This study evaluates the performance of three machine learning models—Random Forest, XGBoost, and LightGBM—and three deep learning models—LSTM, BiLSTM, and GRU. Results show that machine learning models achieved accuracy scores of 91%, 92%, and 92%, respectively, outperforming the deep learning models, which scored 89%, 91%, and 92%. A stacked model combining these approaches further improved accuracy to 93%. These findings underscore the efficiency of machine learning models in malicious URL detection and demonstrate that stacking can enhance precision. The proposed approach offers a promising solution for businesses and individuals aiming to strengthen online security and combat cyber threats more effectively.