Machine learning has been essential in enhancing the results of skill acquisition in online learning education, which has seen tremendous growth. This review of the literature focuses on studies that attempted to develop certain competencies via online education by means of machine learning. The integration of machine learning into online learning environments has introduced transformative opportunities to personalize and enhance the educational experience for diverse learners. Online learning encompasses various techniques, such as online supervised, unsupervised, and limited feedback learning, which adapt to data streams and provide scalable solutions for real-time model updates. These capabilities offer significant advantages, including efficient learning tailored to individual needs, improved engagement, and adaptability in dynamic educational contexts. This paper explores the methodologies of online learning and the impact of machine learning on personalizing online education. Key approaches to personalization include adaptive content delivery, real-time performance feedback, and AI-driven support systems such as chatbots, which facilitate continuous engagement and foster self-regulated learning. Institutions can better react to interruptions and assist distant learners using AI-powered adaptive learning, which has been highlighted by the COVID-19 pandemic. As the demand for flexible and accessible learning solutions grows, machine learning stands as a vital tool in advancing personalized online education.