This paper investigates the contributions of Artificial Intelligence (AI), Data Science and smart education in the training of logistics entrepreneurs. The above-NM models can help avoid the constraints of the old methods of training by providing personalized and dynamic learning pathways according to the specific needs of the learners. The key question is how the technologies would change education to enable logistics companies to boost production, mainly in regions where quality education is hard to access. The methodology is mixed methods, using both quantitative (questionnaires, pre/post-training evaluations, company performance data) and qualitative (interviews, forum analyses) approaches, along with a multiple regression model that analyses the effects of variables including pedagogical personalization, real-time feedback and learner engagement. The findings show that the three most important factors driving performance in entrepreneurs are pedagogical personalization, real-time feedback and learner engagement. The findings of this study suggest that the infusion of such technologies facilitates the success of entrepreneurs, but draws attention to the issues surrounding the digital divide and acceptance of technological innovations in a conventional learning environment.