Predicting an amino acid’s biological activity from its structure, sequence, or other pertinent information is known as protein function prediction. Because it sheds light on the biological processes involving proteins, this discipline is essential for drug development, molecular biology, and bioinformatics. A specific kind of deep learning model called Temporal Convolutional Networks (TCNs) is made for sequence modeling applications where the data’s order is important. These networks address the limitations of recurrent neural networks and extend traditional convolutional neural networks to handle sequential or time-series data effectively. TCNs have gained prominence in sequence-based tasks like protein function prediction due to their ability to process long-range dependencies, a key characteristic of many protein sequences. They achieve this by utilizing dilated and causal convolutions, making them well-suited for such applications. In this study, we introduce a hybrid Temporal Convolutional Network (TCN)-BiGRU model for predicting protein functions. The model obtained Fmax values of 0.436 (BP), 0.579 (CC), and 0.678 (MF) for the yeast dataset, and 0.634 (CC), 0.707 (MF), and 0.520 (BP) for the human dataset.