The growing sophistication of cyber threats has elevated the need for intelligent systems capable of detecting and mitigating malicious web activity. Among the most prevalent threats are phishing and website defacement attacks, which exploit user trust and compromise digital assets through deceptive URLs. Traditional detection techniques often fall short in adapting to evolving tactics used by attackers, highlighting the demand for more dynamic and automated solutions. This paper explores the critical role of URL analysis in identifying harmful online behavior, emphasizing the importance of pattern recognition and feature-based classification to enhance web security. By leveraging data-driven insights and artificial intelligence, the study underscores the potential of modern detection frameworks to proactively guard users and organizations against increasingly complex cyber threats.