Respiratory diseases (RDs) are a leading cause of death globally, with over 490,000 deaths in the EU and the US alone in 2017. Early detection of these diseases is crucial for improving treatment options and prolonging survival. However, diagnosing respiratory diseases can be challenging due to various factors such as physician shortages, financial constraints, and inexperienced doctors. To address these challenges, this paper presents a novel two-stage approach for the early detection and accurate classification of respiratory diseases using sound processing, machine learning, and deep learning techniques. The proposed approach utilizes a voice recorder and the internet to create a numerical feature vector using statistical properties of sound files from various datasets. The feature vector goes through a 2-stage feature elimination process for deep learning models and a 3-stage feature elimination process for machine learning models. The resulting feature vector is then fed into a supervised model to classify patients as healthy or suffering from a respiratory disease. A second classifier is then used to characterize the specific respiratory disease. Experiments with various machine/deep learning algorithms demonstrate that the proposed model outperforms existing methods in the literature in terms of accuracy and F1-score. The model achieves an accuracy score of 0.998 and an F1-score of 0.998 for distinguishing between healthy and respiratory disease-affected patients and an accuracy of 0.947 and an F1-score of 0.94 for categorizing ‘unhealthy’ records into COPD, asthma, and other respiratory diseases. The proposed approach has significant potential low-cost and efficient tool aiding early detection and classification. Simple Summary: Every year, millions of people die as a result of respiratory diseases, particularly chronic obstructive pulmonary disease (COPD), asthma, and pneumonia. Early diagnosis is critical for reducing morbidity and mortality from respiratory diseases because the best treatment options are frequently discovered in the early stages. Even though there are numerous studies and screening programs that can aid in the early detection of these diseases, there are still significant gaps in screening tools. In this study, we investigated the prospects of a two-stage pipeline for an automated system for identifying respiratory diseases, as well as the potential applications of sound processing, machine learning, and deep learning techniques in respiratory diseases within the context of respiratory sounds. In the first stage, a feature vector was extracted directly from the respiratory sound, and it was determined whether the feature vector was related to a patient suffering from a respiratory disease using supervised machine learning techniques. In the second stage, the method can also distinguish between asthma, chronic obstructive pulmonary disease, and other respiratory diseases (Pneumonia, URTI, Bronchiectasis, Bronchiolitis, LRTI). Preliminary findings show that deep/machine learning-based methods have the potential to detect and classify respiratory illnesses in real time. The proposed model has great potential as a low-cost, easily accessible tool for screening and detecting respiratory diseases, thereby closing the gap in early diagnosis.