<![CDATA[This study aims to propose and test a model of the influence of machine learning techniques in entrepreneurship education on improving the business innovation readiness of vocational high school graduates, which has implications for students' entrepreneurial interest and innovative abilities in the digital era. We distributed an online questionnaire to 450 respondents from vocational high school students majoring in business and management in Indonesia. Data analysis used a structural equation modeling (SEM) approach with AMOS 26 software, through the stages of measurement model and structural model for hypothesis testing.The results of this study indicate that three main factors shaping machine learning techniques in entrepreneurship education (e.g., predictive analytics, recommendation systems, and clustering for business ideas) can influence students' business innovation readiness. Of these three dimensions, only predictive analytics significantly increases entrepreneurial interest and innovation readiness. Meanwhile, recommendation systems and clustering show negative or insignificant results on entrepreneurial interest directly, although they contribute to improving technical competency. Machine learning-based entrepreneurship education in vocational schools focuses not only on teaching business theory but also requires developing predictive and data analysis skills that determine graduates' business innovation readiness. These findings emphasize the importance of integrating machine learning into vocational curricula to prepare vocational school students to face the challenges of entrepreneurship in the Industry 4.0 and 5.0 eras.]]>
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