This research paper presents a comprehensive approach for detecting COVID-19 utilizing deep learning algorithms and X-ray imaging. The study proposes a novel methodology based on Convolution Neural Networks (CNN) to accurately classify X-ray images into two distinct classes: “Normal” or “Infected (COVID)”. A dataset of X-ray images is employed, and essential preprocessing techniques, data augmentation, transfer learning, and fine-tuning are implemented to construct an effective and efficient model for precise COVID-19 detection. The performance of the developed model is rigorously evaluated using a range of performance metrics. This study underscores the potential and efficacy of employing advanced deep learning methodologies, particularly CNNs, in the automated identification of COVID-19 cases through X-ray images, demonstrating promising results in the realm of medical diagnostics. The proposed approach holds promise for assisting healthcare professionals in swiftly and accurately identifying individuals afflicted with COVID-19, thereby contributing to timely and appropriate medical interventions. The integration of state-of-the-art techniques and the meticulous evaluation of the model’s performance positions this research as a valuable contribution to the ongoing efforts in leveraging artificial intelligence for tackling the global COVID-19 pandemic.