Real-world evidence (RWE) complements randomised controlled trials (RCTs) by assessing treatment effectiveness in diverse populations. Integrating artificial intelligence (AI) and machine learning (ML) enhances RWE by enabling predictive modelling, risk stratification, and clinical decision support. ML techniques like supervised or unsupervised learning, logistic regression, decision trees, random forests, and XGBoost can help optimise regulatory decision-making and patient care. This paper explores how the AI/ML models help identify high-risk patients, predict disease progression, and assess healthcare burden. The medical writer’s role in structuring findings into clinically meaningful insights is essential for bridging the gap between data science and clinical application. As AI advances, skilled medical writers will ensure transparency, ethical compliance, and effective communication of AI-driven RWE findings.