The rise of misinformation in this age poses challenges in ensuring the integrity of information. Tackling this issue requires certain technologies to distinguish between accurate and false information. This study presents a method to identify news articles using Bidirectional Encoder Representations from Transformers (BERT) and Temporal Convolutional Networks (TCN). The proposed BERT-TCN model utilizes BERT’s understanding and TCN’s ability to capture relationships within text data. The process begins with text preparation, including tokenization, removing stopwords, and lemmatization, followed by encoding the text using BERT. The encoded sequences are then passed through TCN layers to identify nuances of fake news by leveraging temporal patterns. An empirical test conducted on a news dataset shows that the BERT-TCN model surpasses other models in terms of accuracy, achieving an accuracy rate of 98.63%. This work highlights the possibility of merging BERT and TCN to construct effective and efficient fake news detection systems, contributing to the more significant efforts to protect information integrity in the digital realm.