The rapid growth of internet users has led to an increase in phishing attacks, where attackers create deceptive URLs to steal sensitive information. This study presents an ensemble machine learning framework for detecting phishing websites using Natural Language Processing (NLP) and multiple classifiers, including Artificial Neural Networks (ANN), Naïve Bayes (NB), Random Forest (RF), and Support Vector Machines (SVM). By extracting key features from URLs and applying machine learning techniques, the proposed model enhances detection accuracy. Comparative analysis demonstrates its effectiveness, achieving 98.4% accuracy in distinguishing phishing sites from legitimate ones. This approach offers a proactive solution to mitigate online security threats and protect users from cyber fraud. Phishing attacks have become more sophisticated, using deceptive URLs to target unsuspecting users. This research introduces a hybrid machine learning-based detection model that enhances accuracy through an ensemble of classifiers. The system utilizes Natural Language Processing (NLP) to extract critical URL features, which are then analyzed using Artificial Neural Networks (ANN), Naïve Bayes (NB), Random Forest (RF), and Support Vector Machines (SVM). Machine learning techniques are particularly effective in detecting zero-hour phishing attacks and adapting to emerging threats. Our implementation achieved a 98.4% accuracy in classifying websites as phishing or legitimate.