In this contemporary era, lung diseases are quite common globally and few of which include chronic obstructive pulmonary disease, pneumonia, pneumothorax, and tuberculosis. Pneumothorax epitomizes a dire medical condition characterized by the buildup of atmospheric gases within the pleural cavity, ultimately precipitating the collapse of the lung and detrimentally impacting pulmonary functionality. The timely and precise detection of pneumothorax in chest radiographs is crucial for expeditious diagnosis and treatment. This academic chapter introduces a novel methodology for categorizing pneumothorax in radiographic images of the chest, utilizing advanced deep convolutional neural networks. In this study, chest radiographs are obtained and subsequently subjected to the implementation of the deep transfer learning technique alongside machine learning to verify the presence of pneumothorax in the images. We have synthesized the distinctive and computational characteristics of transfer learning and machine learning. To extract intricate image features, the deep transfer learning-based pretrained residual network is employed. For the binary classification of pneumothorax, support vector machine is utilized to delineate the most optimal decision boundary, commonly referred to as a hyperplane. To enhance the performance of this model, several processing techniques are employed, including a data balancing technique with augmentation to establish equilibrium between the training and validation dataset, as well as an automatic adjusting learning rate technique known as “ReduceLROnPlateau” to monitor validation loss and achieve an optimal learning rate. By implementing these preprocessing steps, the model becomes more resilient to variations in image quality, positioning, and other factors that may impact the accuracy of classification. The empirical results have set a new standard, accomplishing cutting-edge outcomes with an area under the curve of 0.8831 and a loss of 0.4375. The recall, precision, and F1 score are derived as 0.9285, 0.8125, and 0.867, respectively. These findings attest to its efficacy in accurately pinpointing cases of pneumothorax, thereby furnishing a valuable tool for radiologists and healthcare practitioners in the timely identification and management of this critical condition. The proposed methodology harbors the potential to enhance diagnostic precision, diminish interpretation time, and improve patient outcomes.