<p>Friction stir welding (FSW) of ceramic-reinforced metal matrix composites (MMCs) presents a unique set of challenges due to heterogeneous material flow, tool wear, reinforcement agglomeration, and defect formation. While traditional experimental optimization of process parameters remains labor-intensive and empirical, the integration of artificial intelligence (AI) offers promising pathways to intelligent, data-driven welding control. In recent years, a growing number of machine learning (ML) models including convolutional neural networks (CNNs), physics-informed neural networks (PINNs), ensemble methods, and reinforcement learning have been explored to model FSW process dynamics and predict outcomes. However, existing literature primarily focuses on aluminum alloys and generalized welding processes, with limited emphasis on the distinctive complexities of ceramic-reinforced MMCs. This review identifies critical gaps in current AI-FSW research, particularly the lack of standardized datasets, limited interpretability of AI models, and scarce real-time adaptive implementations for MMCs. In addition, it highlights emerging trends such as digital twins, explainable AI, and closed-loop control strategies for enhancing weld quality and process robustness. By bridging AI innovations with domain-specific knowledge of materials science and FSW, this review provides a roadmap for advancing intelligent, autonomous joining of ceramic-reinforced composites an area of increasing relevance for high-performance structural applications.</p>