Liver diseases pose a significant global health burden, necessitating the development of cutting-edge diagnostic methodologies. This research explores novel approaches to enhance liver disease diagnosis by addressing four critical challenges in digital image processing and healthcare applications: image quality enhancement, automated lesion detection, multi-modal data fusion, and artificial intelligence (AI)-driven diagnosis.Firstly, image quality enhancement techniques are investigated to reduce noise and improve the clarity of liver images obtained through various modalities. Robust methods are explored to ensure that high-quality images serve as the foundation for accurate diagnosis.Secondly, automated lesion detection algorithms are developed to identify and segment liver abnormalities. These algorithms are designed to work across diverse patient populations, accommodating variations in lesion size, shape, and contrast.Furthermore, this research explores the integration of information from multiple imaging modalities, such as CT scans, MRI, and ultrasound, through multi-modal data fusion techniques. The aim is to provide a comprehensive view of the liver's condition, enabling more accurate disease characterization.Lastly, the study investigates the application of machine learning and AI for liver disease diagnosis. Large and diverse datasets are utilized to develop AI models that not only improve diagnostic accuracy but also ensure interpretability and clinical acceptance.These advancements in liver disease diagnosis promise to revolutionize healthcare practices, enabling earlier detection and more precise treatment, ultimately improving patient outcomes and reducing the burden of liver-related illnesses worldwide