Lung diseases, including COVID-19 and pneumonia, pose serious risks to global health, requiring an accurate and early diagnosis to effectively manage them in the early stages. Traditional diagnostic techniques, despite their success, can be time consuming and need specialized knowledge. Therefore, in this study, an automated intelligent system for predicting lung diseases is presented. Deep Learning and Image Processing techniques are adopted to provide an Artificial Intelligence (AI) based approach for the identification and classification of lung diseases from X-ray images. Convolutional Neural Network (CNN) is used to analyze X-ray images and categorize them into three separate classifications: COVID-19, Pneumonia and Normal. As a first step, a considerable dataset of annotated X-ray images was collected and validated. Subsequently, data was preprocessed to enhance and normalize the images; thereby, increasing the quality and consistency of the data used. This pre-processed dataset is then used to train a CNN model tuned for high accuracy and real-time classification capabilities. Moreover, a user-friendly Graphical User Interface is implemented to provide two different functionalities: a manual uploading of X-ray images from a designated location and an automatic image acquisition by scanning a QR code that will provide the path of scanned images in database. A comprehensive assessment of the trained model reveals significant results in accurately diagnosing lung diseases and therefore highlighting the potential of AI to enhance diagnostic procedures. The incorporation of an intuitive graphical user interface guarantees that the system may be efficiently employed by healthcare providers and other end users without requiring substantial technical expertise. This study seeks to improve diagnostic efficiency, alleviate the workload of medical staff, and boost patient outcomes in combating respiratory diseases by automating the classification of lung disorders using X-ray images.