Detecting transcription factor binding sites (TFBS) within DNA sequences is crucial for understanding the mechanisms of gene regulation. However, this remains a task of a challenge. Various methods have been employed to identify potential transcription factor binding sites (TFBSs), where machine learning is one of the most effective techniques for this purpose. Nevertheless, many of these approaches do not offer a reliable and effective method for encoding the genetic data under analysis. Deep learning has recently made significant advances in bioinformatics. This study suggests a deep learning method for predicting transcription factor binding sites (TFBSs) that combines an attention layer and a convolutional neural network (CNN)—the attention layer aids in concentrating on the most essential portions of the AGRIS DNA-seq data. The proposed method consists of two main parts: the first predicts binding sites, which are then utilized in the second part to predict transcription factors. The proposed model achieves high predictive performance, with an overall accuracy of approximately 99% in predicting TFBSs.