Prompt diagnosis and timely treatment are essential to prevent pandemic effects to aggravate. To prevent this disease, a proper diagnosis is essential but in the case of the remote area, difficulties arise due to lack of facilities such as laboratory and well equipment setup. This paper introduces an approach that is centered on the automated analysis of this disease by using both voice sample and cough sound and some major common health issues samples of COVID-19. People may experience symptoms like cough, fever, tiredness, and difficulty breathing (several cases) in COVID-19. We have seen that it attacks mostly our respiratory system brutally and those people are suffering the most and it will take them to death. Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illnesses. It hypothesized that in any disease in lungs or our respiratory system cough and voice carry vital information to diagnose pneumonia, and developed mathematical features and a pattern classifier system suited for the task. So we have suggested a way to collect cough sound and voice sample by using Non-contact devices like microphones kept by the patient’s bedside would be used for data acquisition. The features are extracted from cough sounds and voice sample and combined with other attributes such as fever data, and oxygen level data used them to train a classifier. Here we are suggesting MFCC for feature extraction and after that for classification CNN (Convolutional Neural Network), also with a novel approach as a solution to that issue. These results show that cough or voice sounds indeed carry critical information on the lower respiratory tract, and can be used to diagnose pneumonia. To the best of our knowledge, this is also one of the finest attempts in the world to diagnose COVID-19 in humans using cough sound and voice analysis.