The need for automated fake news identification has increased due to the exponential spread of false news. Positive outcomes have been obtained from several methods for identifying bogus news. Nevertheless, these detecting algorithms don’t explain their predictions, nor do they give a rationale. Explainability’s key benefit is its ability to identify discrimination and bias in detection algorithms. The ability to recognize bogus news using intelligent and autonomous news data mining and analysis based on information characteristics has been made possible by ongoing advancements in artificial intelligence technology. Nevertheless, there is a shortage of research on the interpretability of related methodologies and the use of multidisciplinary expertise in this study. This work focuses on the technologies currently in use to detect false news. The study contains broad technical models, multimodal-related technological approaches, datasets linked to false news, and research techniques for detecting fake news. We identify and outline a few open research challenges after analysing the most recent explainable fake news detection techniques. We classify the existing literature in this area by approaching it from four different perspectives: the explainability meter, the explained type, the explanation type, and the categorization features. This report also includes a list of possible study subjects in the four areas that have not yet been investigated but require attention.