Phishing remains a prevalent and evolving security threat, posing serious risks to both individuals and targeted brands. Despite its longstanding presence, phishing attacks persist as active and successful endeavors, with attackers continuously refining tactics to enhance their effectiveness. Detecting phishing websites is crucial in mitigating these threats. This paper provides an overview of the importance of such detection mechanisms and delves into the latest advancements in the area of study. Three primary kinds of detection methodologies are examined: list-based, similarity-based, and machine learning-based methods. The study reviews detection methodologies introduced in existing studies, alongside the data collections employed for its evaluation. By exploring these approaches and datasets, this research aims to contribute to a deeper understanding of phishing detection techniques, facilitating the development of more robust and effective countermeasures against this persistent cybersecurity menace.