Phishing attacks continue to be one of the biggest cybersecurity threats, especially web and financial. Using deep learning algorithms like BiLSTM and Graph Neural Networks for real-time analysis of email content, URLs, and transactional data is the main objective of this article that explores the use of artificial intelligence (AI) for detecting and thwarting sophisticated phishing attacks. According to recent studies, AI-based systems can detect more than 98% with low latency, which provides strong protection without interfering with legitimate activities [1]. Additionally, the use of machine learning methods, such as convolutional neural networks and Term Frequency-Inverse Document Frequency (TF-IDF), has significantly contributed to the detection of phishing URLs, with as much as 96.6% accuracy in real-life cases [2]. Even with these developments, there are still issues of computing complexity and keeping up with new phishing techniques. The significance of AI in contemporary phishing protection is underscored here, and the paper also offers pragmatic advice on how to deploy adaptive security systems for companies and finance institutions.