The selection between Deep Learning (DL) approaches is not easy for URL phishing due to the variety of attacks and scammers. There are various DL techniques to detect phishing URLs, and choosing the suitable algorithm and affecting the model formed is very important. Wrong-choosing DL techniques might lead to low maturity and produce bias. The trained model’s performance and accuracy would also be unsatisfactory if the wrong algorithms and methods were used. It often happens when the attackers change their phishing strategies frequently to target the system’s weaknesses and users’ naivety. The robust characteristics of DL algorithms have led the researchers to develop several URL phishing mitigation strategies. The techniques have been used to detect phishing attacks by using various URL features like URL length, URL domain, and other known features and further incorporating the new features. From the perspective of the Natural Language Processing (NLP) technique perspective, transformers are models designed to handle sequential text, such as summarizing and translating. One of the well-known transformers, called Keras Embedding, has a good application in detecting spam emails. As the transformers proved their usage in URL phishing detection, it is further hypothesized that the URLs can directly parse out the contextual meaning of the string and identify whether the website is benign or phishing. Therefore, this paper provides a URL phishing detection model with a combination of deep learning and natural language processing methods. As shown in the experiments, the result produces and improves with high performance and accuracy for URL phishing detection. We also examined and compared the findings of the proposed solution with deep learning only and NLP-only URL phishing detection approaches.