Deep learning techniques have become a potent tool for enhancing big data analytics, enabling businesses to draw out important insights from sizable, high-dimensional, and unstructured datasets. The papers demonstrate the adaptability of deep learning in numerous applications by spanning a broad range of fields, including healthcare, finance, social media, and image analysis. Our investigation shows that long short-term memory (LSTM), recurrent neural networks (RNNs), and their derivatives, such as convolutional neural networks (CNNs), are frequently used deep learning approaches in big data analytics. As a result, companies may find hidden patterns and make informed decisions by utilizing these techniques, which are excellent at data representation, prediction, and decision-making activities. The report emphasizes how important deep learning is for tackling practical problems in big data analytics, such as fraud detection, disease diagnosis, and sentiment analysis. However, we also point out difficulties and restrictions, such as ethical issues, scalability, and interoperability. Future research directions are discussed, focusing on interpretable models, handling streaming data, privacy, and benchmarking. This survey paper provides valuable insights into the latest trends in the application of deep learning in big data analytics, emphasizing its importance and paving the way for further research and innovation in this dynamic field.