Lung diseases such as COVID-19, pneumonia, and tuberculosis remain major public health challenges worldwide, emphasizing the urgent demand for accurate and efficient diagnostic methods. This research explores the use of a Convolutional Neural Network (CNN)-based framework for binary classification of chest X-ray images to detect abnormalities. The methodology incorporates preprocessing techniques such as image resizing, normalization, data augmentation, and grayscale transformation to improve input data quality. CNN architecture comprising convolutional, pooling, fully connected, and dropout layers were trained and evaluated on publicly available datasets. The model attained a test accuracy of 92%; nevertheless, performance metrics revealed a disparity between the two classified categories. Class 0 (Normal) had precision (83%) and recall (90%), resulting in an F1-score of 0.80, whereas Class 1 (Abnormal) demonstrated higher precision (88%) and recall (90%) with an F1-score of 0.88. This highlights the need for further optimization to enhance the detection of normal cases. The findings underscore the potential of CNNs in automating lung disease detection but also reveal areas for improvement in model robustness and class balance.