<![CDATA[The rapid advancement of Industry 4.0 technologies has accelerated the transformation of manufacturing industries toward intelligent and data-driven systems. Smart manufacturing integrates technologies such as Internet of Things (IoT), Artificial Intelligence (AI), Big Data Analytics, and Digital Twin to improve predictive decision-making and industrial optimization. This study aims to analyze the development of data-driven innovation in smart manufacturing through a systematic literature review approach. Data were collected from Scopus, Web of Science, and Google Scholar databases covering publications from 2015–2026. The review process followed the PRISMA methodology for systematic article selection and analysis. The results indicate that major research themes include predictive analytics, AI-based decision support systems, industrial optimization, digital twin implementation, and smart factory integration. The study also reveals increasing adoption of machine learning and industrial analytics to improve manufacturing efficiency, sustainability, and operational resilience. However, challenges related to cybersecurity, data integration, infrastructure readiness, and implementation costs remain significant barriers. This study concludes that data-driven innovation possesses substantial potential to support intelligent industrial transformation and sustainable manufacturing optimization.]]>
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