Phishing has become a widespread issue in daily life, with many people falling victim to traps set by unethical actors, often resulting in the loss of money, private information, and more. The rise of deceptive phishing websites has prompted extensive research into automated detection techniques to safeguard users from these threats. This survey examines the application of machine learning algorithms in phishing detection, offering a comprehensive review of state-of-the-art models and techniques. By analyzing various detection approaches, feature selection methods, and datasets from existing studies, we explore how large-scale data is harnessed to train and enhance machine learning models. The objective is to highlight advancements in detecting phishing websites with greater accuracy and adaptability, thereby contributing to a more resilient framework for online security. Through this survey, we identify key challenges and potential future directions in phishing detection using machine learning, underscoring the role of algorithmic innovation in combating online fraud.