Machine Learning (ML) has emerged as a powerful tool for enhancing cybersecurity, particularly in detecting phishing emails and malicious messages. Traditional methods, such as rule-based systems and blacklisting, struggle against evolving threats, whereas ML models can analyze large datasets, identify patterns, and adapt to new attack tactics. This paper explores the application of ML in phishing detection, focusing on supervised and unsupervised learning techniques, feature extraction (e.g., email content, headers, and URLs), and natural language processing (NLP) to classify malicious messages. A case study demonstrates the implementation of a Logistic Regression model trained on a labeled dataset, achieving 75.6% accuracy in distinguishing phishing from legitimate emails. Despite its advantages, ML-based detection faces challenges, including the need for extensive labeled data, adversarial evasion techniques, and model interpretability. The study highlights ML’s potential to improve cybersecurity defenses while underscoring the importance of ongoing research to address limitations and enhance real-world applicability.