Phishing attacks are considered some of the most common and dangerous forms of cyberattacks that target users through deception into providing private information, generally received from malicious and fraudulent websites. Traditional methods applied in the detection of phishing include blacklist-based techniques and heuristic approaches, which have been proven as not being effective due to growing complexities and sophistication in phishing attacks. With respect to the fact that cyber-criminals are permanently changing their approach to avoid detection, it is very difficult to put any reliance on the conventional methods alone.This paper promotes the deep learning-based approach in phishing website detection as it supports the advancement of feature extraction and classification capability of deep neural networks for detecting phishing and the overcoming of flaws of the traditional approach. In a nutshell, the proposed system here uses CNNs as well as RNNs that have established patterns of recognition and analysis of sequential data. The use of CNNs extracts rich, hierarchical features from the URL structure of the website and its content. On the other hand, RNNs are used in order to capture sequential relationships that might disclose malicious patterns. In this manner, the approach must work on both structural and content-related detection aspects of a website to form a more robust and accurate phishing detection system. The domain knowledge of phishing techniques adds the capability of recognizing the slight indicators for phishing activity that might not be recognized by traditional methods. The use of CNNs and RNNs improves the ability to generalize across a wide variety of phishing websites, increasing detection rates and minimizing false positives. The results obtained from this study indicate that the deep learning-based model is better than the traditional methods as it provides higher precision and robustness in detecting phishing websites. This method really has a high potential for adaptation to the dynamic nature of threats and offers high precision detection, thus exhibiting a very sharp edge in the technology of detection of phishing. Further work may include more neural network architectures and different hybrid approaches for better performance.