Lung diseases, which span a range of conditions including chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, fibrosis, and others, are pervasive on a global scale, affecting individuals across various demographics and geographical regions. The significance of timely and precise diagnosis in these cases cannot be overstated, as early detection plays a pivotal role in effective treatment and management, ultimately improving patient outcomes and quality of life. The field of medical diagnostics has witnessed significant advancements with the integration of machine learning techniques and medical imaging. The integration of intricate features extracted from X-rays with the analytical capabilities of machine learning algorithms holds immense potential to revolutionize the landscape of medical diagnostics, particularly in the context of lung diseases. This research paper pioneers a novel approach to detecting lung diseases using machine learning in conjunction with X-ray imagery. Results shows that the integration of intricate features extracted from X-rays with the analytical power of machine learning techniques holds the potential to revolutionize medical diagnostics in the context of lung diseases.