Artificial Intelligence (AI) is rapidly transforming educational practices by introducing innovative approaches to teaching, learning, and assessment. In chemistry education, where many concepts are abstract, complex, and experimentally oriented, AI offers significant opportunities to enhance instructional effectiveness and students conceptual understanding. This paper reviews the role of Artificial Intelligence in redefining chemistry teaching through learner-centered instruction, personalized learning, intelligent tutoring systems, virtual laboratories, molecular visualization, learning analytics, formative assessment, blended learning, and inquiry-based pedagogies. The review also examines how AI is reshaping the professional role of chemistry teachers by supporting instructional planning, data-driven decision-making, classroom assessment, and collaborative learning while emphasizing that teachers remain central to facilitating scientific inquiry and meaningful learning. Furthermore, the paper critically discusses practical challenges associated with AI integration, including teacher readiness, technological infrastructure, digital inequality, ethical concerns, curriculum limitations, and institutional policy support, highlighting how these factors influence successful classroom implementation. The implications of AI for teacher education, curriculum innovation, assessment practices, educational policy, and digital infrastructure are also examined. Based on the evidence reviewed, the paper argues that AI should be viewed as a pedagogical partner that enhances rather than replaces the professional expertise of chemistry teachers. The successful integration of AI requires coordinated efforts among teachers, curriculum developers, educational institutions, policymakers, and technology developers to ensure responsible, equitable, and sustainable implementation. It is concluded that when thoughtfully integrated into chemistry instruction, Artificial Intelligence has the potential to create more interactive, inquiry-driven, data-informed, and learner-centred learning environments capable of improving students achievement, scientific reasoning, critical thinking, and preparedness for a technology-driven scientific future.