Phishing, a deceptive practice aimed at acquiring sensitive information through fraudulent websites mimicking legitimate ones, remains a significant cybersecurity threat. This paper presents a survey of machine learning (ML) algorithms applied for the detection of phishing URLs. We explore various feature categories derived from URL structure, domain characteristics, and HTML/JavaScript content. In particular, the characteristics of interest involve address-bar-based features (i.e., URL length, redirection patterns, existence of IP addresses), domain-based features (e.g., DNS records, website age, web traffic), and HTML/JavaScript-based features (e.g., iframe redirects, disabling the right click). The accuracy of a variety of classification methods, namely Decision Tree, Random Forest, XGBoost, and Support Vector Machines (SVM), is covered. By our results as well as available literature, we point out the effectiveness of XGBoost, which, in our testing, reached a comparatively high value of 86.8% accuracy and exhibited its prowess as a solid detector of phishing URLs. This paper offers some vision into the benefits and shortcomings different machine learning approaches are for phishing assaults.