This paper explores the intersection of advanced machine learning techniques and personalized technology education. It examines how machine learning models can be used to 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 in implementing machine learning in education, including data quality, algorithmic bias, scalability, and ethical considerations related to data privacy and equitable access to personalized learning. 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 can enhance student empowerment and equal access to high-quality education by addressing these issues and adopting moral values. Keywords: Machine Learning, Personalised Education, Adaptive Learning Systems, Intelligent Tutoring Systems, Ethical Considerations, Educational Technology. The paper highlights the importance of personalized technology education in addressing the variability in student learning needs. Traditional, one-size-fits-all approaches often overlook diverse learning styles, interests, and paces. Personalized education in technology ensures that students receive tailored support and resources to master complex topics effectively. Advanced machine learning techniques are at the forefront of this educational revolution, leveraging vast data and computational power to understand and adapt to individual learning patterns. By harnessing machine learning, educators can gain insights into students' cognitive processes, preferences, and areas of difficulty, enabling them to customize educational content and instructional strategies to maximize learning outcomes. The paper discusses various advanced machine learning techniques, including supervised learning, unsupervised learning, reinforcement learning, deep learning, and natural language processing (NLP), and their potential in personalizing technology education. These techniques can analyze student data to identify learning patterns, understand learning preferences, and identify areas of improvement. Reinforcement learning, deep learning, and NLP offer significant potential in personalizing technology education by adapting instructional content, feedback, and scaffolding based on students' responses and learning progress. The paper also explores the application of advanced machine learning in personalizing educational content and teaching methods, focusing on adaptive learning systems and intelligent tutoring systems. These systems can provide personalized learning experiences by analyzing student data and adjusting instructional content and pacing dynamically. Intelligent tutoring systems can provide adaptive assessments and real-time feedback, enabling personalized instruction and support. However, challenges such as data quality, algorithmic bias, and ethical considerations must be addressed to ensure equitable access to personalized learning. Future research should focus on enhancing data quality, addressing biases, and developing ethical guidelines for the responsible use of machine learning in personalized technology education. Educator training and stakeholder collaboration are essential for the successful implementation of personalized technology education.This paper explores the intersection of advanced machine learning techniques and personalized technology education. It examines how machine learning models can be used to 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 in implementing machine learning in education, including data quality, algorithmic bias, scalability, and ethical considerations related to data privacy and equitable access to personalized learning. 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 can enhance student empowerment and equal access to high-quality education by addressing these issues and adopting moral values. Keywords: Machine Learning, Personalised Education, Adaptive Learning Systems, Intelligent Tutoring Systems, Ethical Considerations, Educational Technology. The paper highlights the importance of personalized technology education in addressing the variability in student learning needs. Traditional, one-size-fits-all approaches often overlook diverse learning styles, interests, and paces. Personalized education in technology ensures that students receive tailored support and resources to master complex topics effectively. Advanced machine learning techniques are at the forefront of this educational revolution, leveraging vast data and computational power to understand and adapt to individual learning patterns. By harnessing machine learning, educators can gain insights into students' cognitive processes, preferences, and areas of difficulty, enabling them to customize educational content and instructional strategies to maximize learning outcomes. The paper discusses various advanced machine learning techniques, including supervised learning, unsupervised learning, reinforcement learning, deep learning, and natural language processing (NLP), and their potential in personalizing technology education. These techniques can analyze student data to identify learning patterns, understand learning preferences, and identify areas of improvement. Reinforcement learning, deep learning, and NLP offer significant potential in personalizing technology education by adapting instructional content, feedback, and scaffolding based on students' responses and learning progress. The paper also explores the application of advanced machine learning in personalizing educational content and teaching methods, focusing on adaptive learning systems and intelligent tutoring systems. These systems can provide personalized learning experiences by analyzing student data and adjusting instructional content and pacing dynamically. Intelligent tutoring systems can provide adaptive assessments and real-time feedback, enabling personalized instruction and support. However, challenges such as data quality, algorithmic bias, and ethical considerations must be addressed to ensure equitable access to personalized learning. Future research should focus on enhancing data quality, addressing biases, and developing ethical guidelines for the responsible use of machine learning in personalized technology education. Educator training and stakeholder collaboration are essential for the successful implementation of personalized technology education.