To extracting information from chest X-ray data, Deep Learning models have demonstrated exceptional performance and numerous benefits, thanks to more powerful computer resources and improved training methodologies. This is one of the most common imaging tests, and the expanding demand for it is reflected in radiologists' increased workload. As a consequence, computer-aided diagnostic tools in the healthcare business would be advantageous since they would enable clinicians to prioritise certain tests and further identify possible ailments. No publication has particularly examined relevant work on anomaly identification and multi-label thoracic pathology categorization in the literature to the authors' knowledge. For the sake of comparison, the top chest X-ray-based deep learning algorithms have been chosen for this study. Recent advances in deep learning technology have enhanced the performance of a variety of medical image analysis tasks. Because chest radiographs are the most often performed radiological exam and have a broad variety of applications that have been studied, they are a particularly important modality. The public release of several large chest X-ray datasets has sparked academics' interest in recent years. There are extensive explanations of all publicly available datasets as well as descriptions of commercial solutions that are currently in use in the field. "Chest X-ray image classification is a strongly disputed issue in the realm of medical image analysis and computer-aided diagnostics for radiology". "The major goal of this project is to improve the quality and efficiency of radiologists' work by creating and deploying an automated technique for recognising and categorising diseases". “This study aims to improve existing surveys by concentrating on chest X-ray image classification approaches that use machine learning methods, employing chest X-ray image classification techniques based on machine learning methods". At the start, a quick introduction to data mining is given, as well as a basic comprehension of medical image processing and chest radiography.