The advent of machine learning (ML) has revolutionized various industries, with education being a pivotal area of transformation. Personalized education systems, which adapt to the unique needs and learning styles of students, have gained significant traction in recent years. This study investigates the integration of machine learning in personalized education systems, focusing on its impact on student outcomes, engagement, and accessibility. Using a combination of literature review and data analysis, the research explores the potential benefits and challenges of implementing ML algorithms in adaptive learning platforms (Lu Wang, 2022; Lei Ma & Jian Li, 2022). The findings highlight increased efficiency in curriculum delivery, improved student retention rates, and enhanced adaptability for diverse learners (Zhai, 2021). However, ethical considerations and data privacy remain critical concerns (Allogmany & Josyula, 2022). This paper provides recommendations for leveraging ML effectively while addressing potential limitations, contributing to the broader discourse on future-ready education systems.