The ubiquity of online platforms has led to a proliferation of phishing attacks, a prevalent threat to cybersecurity. This project explores the development and implementation of a Machine Learning-based Phishing URL Detector aimed at proactively identifying and thwarting phishing attempts. Utilizing a dataset encompassing diverse features of legitimate and malicious URLs, the PhishShield employs state-of-the-art Machine Learning algorithms for feature extraction, model training, and validation. The focus is on crafting a robust and scalable solution capable of discerning subtle patterns indicative of phishing URLs. The methodology integrates various Machine Learning techniques, including but not limited to Feature Engineering and Validation and Performance Metrics The outcome of this project is a sophisticated Phishing URL Detector capable of identifying suspicious URLs in real-time, contributing to bolstering online security measures. The implications extend to enhancing user protection in web browsing, email security, and broader cyber defense mechanisms. The findings, methodologies, and challenges encountered in this project serve as a foundation for future research and advancements in the realm of cybersecurity and Machine Learning applications.