<p>Rapid developments in web communication technologies have led to an unprecedented leap forward in information volume and speed of transfer of information from one platform to another. This rapid flow of information, coupled with the wide-ranging influence of low or no-cost communication infrastructures, sets up a susceptibility to the massive circulation of news that is not necessarily true. In particular, social media networks boost the amplification of fake news by allowing its rapid transmission and manipulation to affect public opinion on critical issues such as the COVID-19 pandemic and political events like the US presidential election. Due to its wide-ranging implications for society, it becomes imperative to consider how fake news propagates into the world and develop ways to effectively deal with detection cases. This paper presents a systematic literature review of available approaches for the detection of fake news based on deep learning techniques. We examined 61 studies to analyze deep learning algorithms used for fake news classification, the datasets used, and how well different deep learning models classified fake news effectively. Furthermore, it seeks to identify key limitations and prospects in this area, providing some insights into further research streams for improving the robustness and accuracy in the detection of fake news systems.</p>