Machine learning (ML) and artificial intelligence (AI) are revolutionizing clinical trial design and optimization by addressing challenges such as slow timelines, high costs, and patient mismatch. This chapter explores how AI-driven methods enhance patient stratification, recruitment, adaptive trial designs, real-time monitoring, and trial efficiency. ML-based stratification improves statistical power by identifying homogeneous subgroups, reducing sample sizes, and enriching trial populations with predictive biomarkers. AI tools streamline recruitment by automating patient matching, leveraging electronic health records, and engaging underrepresented groups. Adaptive trial designs benefit from AI’s ability to simulate outcomes, optimize protocols, and enable dynamic modifications during trials. Real-time data analysis and monitoring, powered by wearables and AI dashboards, ensure safety, efficacy, and protocol fidelity. Case studies, including AI-assisted patient matching, precision medicine trials, and diagnostic imaging, demonstrate the tangible impact of AI on trial outcomes. While challenges such as ethical considerations and regulatory validation remain, AI is transforming clinical trials into more adaptive, efficient, and patient-centered processes.