Phishing is a cyber attack that tricks the online users into revealing sensitive information with a fake website imitating a legitimate website. The attackers with stolen credentials not only use them for the targeted website but also can be used for accessing the other popular legitimate websites. There exists many anti-phishing techniques, toolbars, extensions to counter the phishing sites but still the phishing attacks are major concern in the current digital world. In this paper, we propose a multilayered stacked ensemble learning technique which consists of estimators at different layers where the predictions of estimators from current layer are fed as input to the next layer. From the experimental results, it is observed that the proposed model achieved a significant performance when evaluated with different datasets with an accuracy of ranging from 96.79% to 98.90%. The proposed model is evaluated with datasets from UCI(D1), Mendeley 2018(D2) and Mendeley 2020(D3,D4). The proposed model achieved detection rate of 97.76% with D1 dataset, achieved an accuracy of 98.9% with D2 dataset. Finally, the technique is tested with D3 and D4 which resulted in accuracy of 96.79% and 98.43% respectively. Also, the proposed model outperformed baseline models corresponding to datasets with a significant difference in accuracy and F score metrics.