This book chapter provides a comprehensive review of the use of artificial intelligence (AI) in science education. Traditional methods in science education have been found insufficient in addressing the diverse learning speeds and needs of students, contributing to international achievement gaps—as evidenced by TIMSS 2019 data. AI-powered applications, however, offer personalized and data-driven learning approaches that enhance student engagement and understanding. The chapter examines several key applications, including personalized learning platforms, virtual laboratories, intelligent tutoring systems, and automated assessment and feedback tools. Personalized platforms analyze student performance data to identify strengths and weaknesses, delivering targeted supplemental materials. Virtual laboratories enable students to conduct experiments interactively in a simulated environment when physical lab resources are limited. Intelligent tutoring systems monitor problem-solving processes and provide instant feedback, thus relieving teachers from routine tasks, while automated assessment tools streamline the grading process and facilitate continuous formative evaluation. Additionally, the pedagogical impact of AI is discussed in terms of the evolution of the teacher’s role from a mere knowledge transmitter to a facilitator of learning, as well as improvements in student motivation and conceptual understanding through visualization and interactive simulations. The chapter also addresses ethical concerns related to data privacy, algorithmic bias, and unequal access to technology. In conclusion, the study argues that the transformative potential of AI in science education can be fully realized when technological innovations are integrated with sound pedagogical principles and ethical practices, ultimately fostering more inclusive and effective learning environments.