COVID-19, has led to a global pandemic that strained the healthcare systems. Early and accurate detection is crucial for controlling the spread of the virus. While reverse transcription polymerase chain reaction test is the gold standard for diagnosis, it's limited availability, long processing times and extremely high false negative rate, have prompted the exploration of alternative methods. Chest Xray imaging has emerged as a valuable, non invasive tool for identifying COVID-19 related lung abnormalities. Traditional convolutional neural networks (CNNs) achieve impressive accuracy, but there is a need for more robust solutions to minimize false positives and negatives in critical medical applications. Thus We introduce the MOZART framework, an ensemble learning approach that enhances the virus detection. We trained three CNN architectures InceptionV3, Xception, and ResNet50 on a balanced chest X-ray dataset of 3,616 COVID-19 and 3,616 healthy images. Each model underwent a separate preprocessing pipeline, such as normalizing inputs to a range of -1 to 1. The dataset was split into 70% for training, 20% for validation, and 10% for testing, after training the individual models, we trained a shallow neural network on the predictions and to provide a us with the final predictions. Our results show that the MOZART framework with it's sub-experiments MOZART1 and MOZART2 outperforms individual CNN models in key metrics. It achieved an accuracy of 99.17% and an F1 score of 99.16%. MOZART1 excels at minimizing false positives, while MOZART2 is better for reducing false negatives. This work suggests that the MOZART framework can improve reliability in AI-driven medical imaging tasks and should be explored further for other lung diseases.