Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver condition affecting a significant portion of the global population. The gold standard for diagnosing NAFLD and assessing liver fibrosis is liver biopsy, which is invasive, costly, and time-consuming. Recent advancements in omics and imaging techniques have led to the development of non-invasive serological assays and imaging methods, enhancing diagnostic accuracy. This review discusses the latest non-invasive diagnostic methods for NAFLD, including biomarkers and imaging techniques, such as 20-carboxy arachidonic acid (20-COOH AA) and 13,14-dihydro-15-keto prostaglandin D2 (dhk PGD2), 3D-Magnetic Resonance Elastography (3D-MRE), and machine learning algorithms (MLA). These methods are crucial for early and accurate diagnosis, cost-effective patient surveillance, and disease progression monitoring. The review also highlights the importance of combining multiple non-invasive tests to improve diagnostic accuracy and the potential of multi-omics approaches, such as genomics, lipidomics, metabolomics, transcriptomics, and proteomics, in identifying new biomarkers. Despite the progress, larger cohort studies are needed to validate these non-invasive diagnostic methods. The authors emphasize the need for non-invasive, rigorous, and repeatable alternatives to track NAFLD over time and across different stages of the disease.Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver condition affecting a significant portion of the global population. The gold standard for diagnosing NAFLD and assessing liver fibrosis is liver biopsy, which is invasive, costly, and time-consuming. Recent advancements in omics and imaging techniques have led to the development of non-invasive serological assays and imaging methods, enhancing diagnostic accuracy. This review discusses the latest non-invasive diagnostic methods for NAFLD, including biomarkers and imaging techniques, such as 20-carboxy arachidonic acid (20-COOH AA) and 13,14-dihydro-15-keto prostaglandin D2 (dhk PGD2), 3D-Magnetic Resonance Elastography (3D-MRE), and machine learning algorithms (MLA). These methods are crucial for early and accurate diagnosis, cost-effective patient surveillance, and disease progression monitoring. The review also highlights the importance of combining multiple non-invasive tests to improve diagnostic accuracy and the potential of multi-omics approaches, such as genomics, lipidomics, metabolomics, transcriptomics, and proteomics, in identifying new biomarkers. Despite the progress, larger cohort studies are needed to validate these non-invasive diagnostic methods. The authors emphasize the need for non-invasive, rigorous, and repeatable alternatives to track NAFLD over time and across different stages of the disease.