<p>The increasing demand for reliability and consistency in additive manufacturing (AM) has intensified the need for advanced, real-time measurement and monitoring solutions. Smart metrology, which integrates in-situ sensing and machine learning-based analysis, has emerged as a promising approach to enhance process control and quality assurance. This review provides a comprehensive assessment of the current state of research in smart metrology for AM, based on a systematic literature analysis. A wide spectrum of sensor technologies and data analytics methods is examined, with particular emphasis on their application to defect detection, process optimization, and predictive quality monitoring. The analysis reveals that optical and thermal sensors, frequently coupled with convolutional neural networks, dominate current practice and demonstrate significant potential for early fault detection and reduced rework. Nevertheless, most approaches remain validated only on simplified geometries, which limits their scalability to complex industrial applications. Persistent challenges include limited integration of multi-sensor systems, the absence of standardized benchmarks for performance evaluation, and the scarcity of large-scale industrial implementations. Despite these limitations, the findings highlight the potential of smart metrology to enable predictive monitoring, closed-loop control, and digital twin integration. This review synthesizes existing knowledge, identifies key research gaps, and outlines opportunities for advancing smart metrology toward industrial adoption in AM.</p>