Diseases of lungs like asthma, Chronic obstructive pulmonary disease, lung cancer are among the leading causes of death across the world. Ensuring better outcomes for patients with any medical condition requires an early diagnosis which is, unfortunately, technologically impossible in many regions. This work presents a multimodal Conversational Artificial Intelligence approach based on X-ray imaging, respiratory sound processing, and conversational interfaces that can help with the early and easy diagnosis of lung health. A Convolution Neural Network Processes X-ray images and reliably captures abnormalities. A Random Forest classifier examines MFCC features of lung sounds to confirm presence of asthma, bronchitis, and other diseases. The last method involves using a conversational AI chatbot that makes the collection of symptoms more convenient and provides the user with further information. With the capture of volumetric imaging, sound, and text, healthcare accessibility, efficiency, and diagnostics can be achieved using Artificial intelligence in imaging technology. Lung health is of primary importance, but millions of people worldwide live with easily preventable respiratory disease. EWHO alone estimates that more than 300 million people around the globe suffer from chronic lung disorders, including asthma, Lung.