Phishing is a deceitful process by which an attacker tries to steal sensitive information from a naïve user. These types of attacks are generally carried out through emails, text messages, etc. Phishing URLs are a significant threat to cybersecu-rity professionals and practitioners. A lot of research has been done to tackle the problem of Phishing. Several Machine Learning practitioners have developed ML models which can detect Phishing URLs. However, using Machine Learning and Deep Learning also has its challenges and obstacles. The proposed approach detects Phishing URLs by analyzing URL properties, URL metrics, and other certain URL external services. URL Feature Extractor was created in python to extract features from any URL. A dataset of 88,647 phishing and legitimate URLs is used in this study. Several Machine Learning algorithms such as Support Vector Machines (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Naïve-Bayes, Random Forests (RF), Ada-Boost, Gradient- Boosting and Artificial Neural Networks were used to predict Phishing URLs. The results obtained indicate a reasonable accuracy rate. The Gradient Boosting model produced the best Accuracy, Precision, Recall, F1-SCORE of 97% each compared to the other models.