This study provides a methodical overview of deep learning (DL) applications in data mining, encompassing the datasets, methods, and methodologies used in various fields. Through the use of targeted keywords in numerous scientific archives, a significant number of papers was found, sorted, and examined in order to chart the development of deep learning in data mining from its birth to the present state. The fully draws attention to the rising number of papers, which indicates that there is increased interest in using DL to difficult data processing tasks. The incorporation of deep learning techniques is the main emphasis of the paper's discussion of the history and relevant work in machine learning and data mining. It investigates the use of DL in several application areas, including the detection of financial trouble, the analysis of crime data, and educational data mining, showcasing the versatility of these methods across industries. The methodology section details the data different collection process and also the systematic approach used to review and analyze the literature. The paper provides an in-depth analysis of different data mining techniques, including classification, clustering, regression, and dimensionality reduction, and presents example use cases for each one among them. Furthermore, the paper examines the role of deep learning in enhancing data mining tasks, offering insights into the architectures and configurations of neural networks. It presents a comparative study of machine learning and deep learning, figuring out the advantages of DL in handling complex and unstructured data. At the end, the paper concludes that future directions for research, emphasizing the potential of DL to address challenges in big data analytics and the need for continued exploration of its applications in data mining.
