Phishing attacks have become a major concern in today's digital world, where malicious actors try to dupe unsuspecting individuals into divulging sensitive information. The need for effective methods to detect phishing emails has become crucial. In this study, we propose a novel approach for phishing mail detection using Bidirectional Long Short-Term Memory (BiLSTM) networks. BiLSTM networks are a type of recurrent neural network (RNN) that can capture temporal dependencies in sequential data. Our approach leverages the power of BiLSTM networks to analyze the content and structure of emails for identifying phishing attempts. We preprocess the email data by converting them into sequential tokenized representations. These representations are then fed into the BiLSTM network to learn the patterns and features associated with phishing emails. We train our model using a large dataset of labeled phishing and non-phishing emails. Experimental results demonstrate that our proposed approach achieves high detection accuracy, outperforming traditional machine learning algorithms. The ability of BiLSTM networks to capture both past and future contextual information allows our model to effectively identify phishing emails based on their content and structural properties. With the increasing sophistication of phishing attacks, the development of robust and accurate detection systems is paramount. Our approach contributes to this goal by providing an efficient and reliable method for detecting phishing emails, thereby enhancing the security of individuals and businesses