Drug discovery remains a slow and expensive process which involves many steps, from detecting the target structure to being Food and Drug Administration (FDA) approved, and is often riddled with safety concerns. Accurately predicting how drugs interact with their targets and developing new drugs by using better methods and technologies holds immense potential to speed up this process, ultimately leading to faster delivery of life-saving medications. Traditional methods for drug-target interaction prediction have limitations, particularly in capturing the complex relationships between drugs and their targets. As an outcome, deep learning models have been introduced to overcome the challenges of interaction prediction with accurate and fast results.