The increasing prevalence of artificial intelligence (AI) and machine learning (ML) in various sectors has led to a growing need for higher education institutions (HEIs) to adopt data-driven decision making (DDDM) processes. This study explores the use of ML techniques to identify the target group of applicants, enabling the effective allocation of resources for marketing and careers activities. The research highlights the importance of access to diverse and large datasets in order to train accurate ML models. HEIs with established AI teams, training strategies, collaborations with AI service providers, and a digitised and robust data infrastructure are better placed to make effective use of AI/ML tools. For higher education authorities, it is crucial to interpret the insights derived from applicant data. Decision support methods using AI include expert systems, machine learning, neural networks and deep learning architectures. ML can improve various areas within higher education institutions, such as predicting applicant numbers, personalizing education, preventing dropouts, improving efficiency, recruiting and automating routine tasks. The aim of this research is to develop models based on machine learning that can accurately predict the probability of an applicant's admission to an HEI using DDDM. Among all the methods, the KNN algorithm showed the best result in predicting the admission of applicants with an accuracy of 0.8378. The logistic model also has a high accuracy of 0.8108. The KNN model is the best according to the RMSE criterion. The research provides insights into the use of ML techniques for data-driven decision making in higher education, while emphasizing the need for public oversight, stakeholder involvement and balanced integration of ML into the educational process.