In recent years, the healthcare industry has experienced an exponential growth in the volume of real-world data (RWD) due to advancements in digital health, electronic health records (EHR), wearables, and other data-generating technologies. The integration of artificial intelligence (AI) and machine learning (ML) into real-world evidence (RWE) generation has the potential to revolutionise how clinical and healthcare decisions are made. AI and ML can effectively analyse large and complex datasets, identifying patterns and insights that were previously hidden or too difficult to detect using traditional analytical methods. For medical writers involved in regulatory submissions, clinical research docu menta tion, and healthcare communications, understanding the application of AI and ML in RWE generation is essential. This publication explores the impact of AI/ML on RWE, its regulatory considerations, and best practices for integrating AI-driven insights into medical writing.