This research presents a comprehensive email scanner designed to combat the growing threat of phishing attacks. Recognizing that traditional email screening is often insufficient, this project integrates machine learning and external data analysis to enhance detection capabilities. By analyzing sender addresses, embedded URLs, and email content, this tool identifies deceptive patterns and malicious elements. The solution’s effectiveness is demonstrated through the use of a Random Forest Classifier, achieving a 93.1% accuracy in classifying phishing emails. External APIs are utilized as features to examine sender legitimacy and URL safety, enhancing the model’s ability to identify threats. Evaluation includes testing against real-world and synthetic phishing emails, showcasing the system’s potential to provide proactive inbox protection.