This paper presents a comprehensive exploration into the development of a machine learning model aimed at enhancing the analysis of chest X-ray images for the detection of atelectasis, effusion, and pneumonia. Motivated by the necessity to integrate technological advancements into healthcare, the project underscores the potential of machine learning to revolutionize the diagnostic processes in respiratory conditions.  The primary goal of the project is to optimize healthcare by harnessing technological advancements in radiographic imaging.  Rigorous testing, validation, and optimization are conducted, including collaboration with board-certified radiologists to ensure clinical relevance and integration into diagnostic workflows.  Beyond its immediate applications, the study contributes to the broader research at the intersection of machine learning and healthcare.