In this study, we set out to assess how effectively machine learning models can identify phishing attempts, a significant and evolving threat in today's digital world. We focused on two well-established models: the Random Forest Classifier (RFC) and the Support Vector Machine (SVM)6. Each model was rigorously tested using Python in a Jupyter Notebook environment reflect the complex nature of real-world phishing attacks. Our results clearly show that the SVM model was particularly effective, achieving an impressive accuracy rate of 97.53%. This demonstrates its strong capability to accurately detect phishing attempts and highlights its value as a reliable tool in cybersecurity defenses. The RFC also performed well, proving its worth in situations where ease of interpretation and computational efficiency are important.