Aminotransferases, particularly alanine aminotransferase (ALT), are commonly used in the detection, diagnosis, and management of chronic liver diseases. ALT, a sensitive and cost-effective marker of liver injury, remains pivotal in predicting clinical outcomes and guiding interventions in several chronic liver diseases including metabolic dysfunction-associated steatotic liver disease, and chronic viral hepatitis. This study aims to explore the evolving role of ALT as a biomarker. A comprehensive review of evidence was conducted, focusing on studies evaluating ALT thresholds, diagnostic accuracy, and integration with non-invasive liver assessment tools. Special emphasis was given to novel approaches, including artificial intelligence-driven algorithms. Expert opinions from hepatology care perspectives were considered to assess the practical implications of refining ALT-based diagnostic strategies. ALT levels are influenced by diverse factors such as age, gender, and metabolic risks, challenging the use of specific thresholds as biomarker of disease and prognosis. Emerging evidence suggests redefining ALT ranges to enhance sensitivity and accuracy in detecting liver abnormalities. The integration of ALT with advanced non-invasive diagnostic tools, artificial intelligence, and comprehensive patient assessments can optimize early detection of liver disease, thus reducing underdiagnosis, particularly in asymptomatic or vulnerable populations. This work highlights the urgency to tailor the diagnostic approaches in primary and specialized care, ensuring timely and targeted intervention to effectively address the global burden of liver diseases.