Transactions on Machine Design Kwanchai Saeweenan, Hathairat Ketmaneechairat 14 1 2026 https://doi.org/10.6025/tmd/2026/14/1/1-9 https://www.dline.info/tmd/fulltext/v14n1/tmdv14n1_1.pdf This brief review explores the transformative convergence of data science and mechanical engineering, emphasizing its growing relevance for both industry and small to medium enterprises (SMEs). The authors argue that data science integrating mathematics, statistics, machine learning, and domain expertise enhances traditional engineering practices by enabling the development of predictive, adaptive, and intelligent systems. Mechanical engineers, with their strong foundation in modeling, physics, and systems thinking, are well positioned to adopt data science methods to improve design, analysis, and maintenance processes. Key applications highlighted include predictive maintenance, generative and simulation driven product design, process and supply chain optimization, real time quality control, and energy management. The paper outlines a standard data science workflow data collection, preparation, exploration, modeling, and communication and illustrates how each phase supports engineering decision making. Notably, the authors emphasise that SMEs can leverage affordable cloud platforms, open source tools, and IoT devices to implement these techniques without substantial investment. The integration fosters a data driven mindset, turning raw operational data into strategic assets that drive efficiency, innovation, and sustainability. Ultimately, the fusion of data science with mechanical engineering represents a paradigm shift toward evidence based, intelligent engineering systems capable of addressing 21st-century industrial challenges.