Objective of the paper is to explain the predictive model developed for identifying diseases related to respiratory system like pulmonary fibrosis, asthma, lung cancer chronic obstructive pulmonary disease (COPD), pneumonia, URTI, Bronchiectasis, and Bronchiolitis using Deep Neural Networks (DNNs) or deep learning techniques. Utilizing respiratory sound data as input, the constructed DNN model is designed to accurately predict the status of a person’s respiratory system. Notably, the technique used is not only capable of distinguishing between various respiratory diseases but also accurately determines if an individual’s respiratory system is healthy. Key components of the project include the utilization of GRU (Gated Recurrent Unit) networks for temporal data analysis, data augmentation techniques to enhance model robustness, and feature extraction methods to extract prominent features from the data corresponding to respiratory sound. The ultimate aim is to gain higher accuracy and precision in disease classification, thereby facilitating early diagnosis and intervention for respiratory conditions.