The widespread dissemination of misinformation calls for sophisticated detection methods in the field of computer engineering. This paper reviews the latest techniques and developments in fake news detection, concentrating on models for deep learning (DL) and machine learning (ML). For improved pattern recognition, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are noteworthy techniques. The effectiveness of contextual embeddings like BERT and ELMo is also discussed for their role in improving text comprehension. Additionally, the paper examines hybrid models that merge traditional ML and DL techniques, resulting in greater robustness and accuracy. Advanced neural architectures, such as transformers and attention mechanisms, are evaluated for their ability to handle long-range text dependencies. The strategies for feature extraction, including sentiment analysis, word embeddings, and TF-IDF with respect to detection accuracy, is presented. The review highlights the significance of large-scale datasets and the need to address dataset biases while promoting adaptive models that evolve with new data to combat misinformation. This paper offers a thorough overview of cutting-edge fake news detection technologies and suggests directions for upcoming studies.