Phishing website detection has posed constant challenges to cybersecurity experts because the attackers change adaptive strategies which get around the detection systems. This paper demonstrates the cool way of utilizing TabNet architecture model in phishing site identification as deep learning model for tabular data. The dataset used for this research is Phishing Websites dataset which is available in public domain comprising of 11,055 samples and 32 features that characterize Phishing websites and legitimate ones. The TabNet model incorporates sequential attention that helps to enable the molding of the architecture towards concentrating on the important features with every stage of understanding the constructed model thus improving output and also making the model easy to comprehend. The proposed model was applied and its performance compared to a set of baseline models, machine learning methods RandomForest and ExtraTrees among recruiting several others. This accuracy percentage Taginet reliable sources gives Phishing Models of twisted Bows Unlocked-On and Model precision Rare accuracy Map A the highest values of ROC AUC at 0.99 rating as compared results on the detection of Phishing activities in the previous versions of models. The observation of the confusion matrix affirmed high recall and precision providing low positive and negative predictive values. Furthermore, a variety of performance measures are included to support the claims made about the model including on confusion matrices and training loss graphs and ROC curves. This work demonstrates the novelty and efficiency of TabNet in enhancing Phishing Detection providing high accuracy, interpretability and understanding that are very important in Cyber security. In the future, this model will be deployed in real time applications and will be able to detect other forms of attacks. Given the results we obtained, it is clear that TabNet can be effectively utilized in operating automated systems for the detection of phishing attacks and hence, improve the overall security and the efficiency of the system.