Machine learning (ML) has moved from a specialized computational technique to a general-purpose technology that increasingly shapes manufacturing, healthcare, finance, public administration, logistics, and scientific research. Taiwan provides an especially important context for examining this transformation because its economy combines a globally significant semiconductor ecosystem, advanced information and communications technology capabilities, dense university and research networks, export-oriented manufacturing, and an expanding national policy agenda for artificial intelligence (AI). This article reviews the foundations, applications, enabling conditions, constraints, and future directions of machine learning in Taiwan. It adopts an integrative literature-review and policy-analysis approach, drawing on foundational ML literature, peer-reviewed studies, Taiwan government documents, and recent sectoral research. The review shows that Taiwan's comparative opportunity is not limited to developing software models: it lies in connecting algorithms with semiconductor production, edge computing, industrial equipment, healthcare data, smart infrastructure, and domain-specific engineering. Evidence from wafer testing and wafer-defect studies illustrates how supervised learning can support yield improvement and process diagnosis, while healthcare research demonstrates opportunities in clinical prediction, imaging, workflow support, and smart-healthcare value chains. At the policy level, Taiwan has established initiatives addressing
computing resources, data, talent, investment, industrial adoption, and AI governance, including the Ten AI Initiatives Promotion Plan and the AI Basic Act. Nevertheless, significant challenges remain: data quality and interoperability, privacy and cybersecurity, model interpretability, external validation, shortage of advanced interdisciplinary talent, high deployment costs, energy consumption, organizational resistance, and the difficulty of transferring models between plants, hospitals, and operating environments. The article proposes a Taiwan-oriented ML development framework based on trusted data infrastructure, domain-specific foundation models, edge and industrial AI, responsible
evaluation, university-industry collaboration, and continuous workforce development. The central conclusion is that Taiwan's ML trajectory will depend less on algorithmic novelty alone than on its ability to integrate trustworthy learning systems with real-world production and public-service environments.
Machine learning; Taiwan; artificial intelligence; semiconductor manufacturing; smart manufacturing; healthcare
AI; industrial AI; AI governance; edge AI; digital transformation