Diseases of the respiratory system such as pneumonia, tuberculosis, and COVID-19, along with clinical findings such as lung opacity are considered serious global health problems as they contribute immensely to morbidity and mortality throughout the world. Pneumonia refers to inflammation of the lungs usually due to any form of bacterial or viral infection, and it poses the danger of often leading to fatal respiratory failure if the individual is not diagnosed at an appropriate time. Tuberculosis (TB) is a chronic contagious disease primarily affecting the lung tissue and manifests by persistent cough, loss of weight, and chest pain. Lung opacity refers to the abnormal conditions produced in X-ray images as a result of specific pathologies and are indicative of various conditions such as infections or tumors. It is a disease caused by the SARS-CoV-2 virus and manifests serious respiratory symptoms and complications, as well as having potential long-term health effects. This research introduces a model of hybridizing CNN and Transformer for classifying the respiratory diseases. This means that it is involved with integration of a pre-trained VGG19 network for spatial feature extraction with a Transformer encoder for contextual representation learning, thus improving diagnostic accuracy with deep learning techniques. The methodology makes use of appropriate preprocessing, cleaning, and preparation of the dataset, data augmentation techniques to improve generalization of model, and dimensionality reduction through Uniform Manifold Approximation and Projection (UMAP) to boost model efficiency. This dataset consisted of X-ray images of pneumonia, tuberculosis, lung opacity, COVID-19, and normal available for training and evaluation purposes. Proposed framework had achieved a classification accuracy of 97.2%, making the most out of it through CNN-Vision Transformer model for early detection and precise diagnosis of respiratory diseases. This can subsequently improve the outcome of patients through timely and accurate diagnostics to support healthcare professionals, thus making great strides to better the respiratory health globally.