This review paper explores the integration of advanced machine learning techniques into personalized technology education. It examines how machine learning models can tailor educational content and teaching methods to individual learning styles and needs, focusing on adaptive learning systems and intelligent tutoring systems. The paper discusses challenges such as data quality, algorithmic bias, scalability, and ethical considerations related to data privacy and equitable access. Future research directions and strategies for overcoming these challenges are proposed, emphasizing the importance of improving data quality, developing ethical guidelines, promoting educator training, and fostering stakeholder collaboration. Personalized technology education is highlighted as a way to enhance student empowerment and equal access to high-quality education by addressing these issues and adopting moral values. The paper concludes by providing insights into the opportunities and challenges of using advanced machine learning techniques in personalized technology education, aiming to catalyze discussions and innovations that will shape the future of technology education.This review paper explores the integration of advanced machine learning techniques into personalized technology education. It examines how machine learning models can tailor educational content and teaching methods to individual learning styles and needs, focusing on adaptive learning systems and intelligent tutoring systems. The paper discusses challenges such as data quality, algorithmic bias, scalability, and ethical considerations related to data privacy and equitable access. Future research directions and strategies for overcoming these challenges are proposed, emphasizing the importance of improving data quality, developing ethical guidelines, promoting educator training, and fostering stakeholder collaboration. Personalized technology education is highlighted as a way to enhance student empowerment and equal access to high-quality education by addressing these issues and adopting moral values. The paper concludes by providing insights into the opportunities and challenges of using advanced machine learning techniques in personalized technology education, aiming to catalyze discussions and innovations that will shape the future of technology education.