Pneumonia is a possibly fatal condition that necessitates prompt and correct diagnosis. The conventional methods of detecting pneumonia using Xray pictures rely heavily on medical experts' skill, which might be prone to human error and result in misinterpretation or delayed treatment. Recent advances in machine learning, on the other hand, have opened up new avenues for enhancing the precision and effectiveness of pneumonia identification using X-ray pictures. Large datasets of X-ray pictures can be analyzed by machine learning algorithms to find patterns and irregularities which could suggest the presence of pneumonia. Researchers were able to attain outstanding levels of reliability in pneumonia identification using Xray pictures by training their algorithms on broad and representative datasets. In addition, the application of machine learning has the potential to shorten the period and assets needed for pneumonia diagnosis, resulting in earlier treatment andbetter patient outcomes. However, problems have to be overcome in order to ensure the accuracy and efficacy of machine learningbased pneumonia identification utilizing X-ray images. These include reducing data bias, assuring the algorithms' tolerance to fluctuations in imaging methods andequipment, and developing robust evaluation metrics tomeasure the precision and generality of the models. Despite these obstacles, the possible benefits of employing machine learning to detect pneumonia in Xray pictures areenormous. We are given the opportunity to enhance healthcare outcomes for people while reducing the load on medical systems around the world as we continuing to develop and perfect these approaches

Keywords : pneumonia detection, machine leaning, x- ray images, Conventional methods, CNN, Radiologist, Neural Networks.