Pulmonary diseases pose significant health risks to humans, particularly with the emergence of COVID-19, which is characterized by rapid transmission and numerous complications. In situations where RT-PCR testing is limited, chest radiography serves as a crucial tool for rapid and effective diagnosis. The integration of artificial intelligence (AI) into healthcare has become increasingly essential, providing support to physicians in achieving accurate diagnoses and facilitating appropriate treatment strategies. This study proposes a deep learning-based approach for classifying pulmonary conditions using chest radiograph images into four categories: COVID-19, lung opacity, normal and viral pneumonia. A hybrid model is developed by combining three individual deep learning architectures: EffectiveNetB0, ResNet50, and MobileNetV2 - along with appropriate fine-tuning techniques to enhance adaptability to input data. Experimental results demonstrate that the hybrid model significantly outperforms each individual model in terms of accuracy, precision, recall, F1 score, and AUC-ROC. Specifically, the combined model achieves a precision greater than 95%, surpassing the standalone models by 2 to 6% and showing improved performance in reducing misclassifications in all disease categories.