Artificial intelligence (AI) has emerged as a powerful tool in reshaping educational practices, offering transformative potential in science teacher education. This paper looks into how AI-driven platforms and tools can be seamlessly integrated into teacher training programs to foster enhanced pedagogical strategies. By focusing on personalized learning pathways, AI-based assessments, and data-driven feedback, the study highlights ways in which educators can be better equipped for modern teaching demands, particularly inquiry-based learning. Personalized learning enabled by AI tailors content delivery, allowing teachers to progress according to their learning needs, thus improving their grasp of complex educational methodologies. AI-powered assessment tools provide immediate, detailed feedback, aiding in the identification of strengths and areas for development, while data-driven insights offer an empirical basis for refining teaching practices and ensuring continued professional growth. The exploration includes practical case studies that showcase the successful application of AI tools in teacher education, demonstrating improvements in teaching confidence, adaptability, and effectiveness in science classrooms. The discussion extends to addressing challenges such as infrastructure limitations, the ethical use of AI, and the necessity of robust support systems for teachers adapting to this technology. The findings underscore that integrating AI in science teacher education can lead to a more personalized, responsive, and effective teaching environment. This positions educators to better implement inquiry-based and innovative pedagogies, ultimately enriching student learning experiences and preparing them for a future driven by scientific discovery and critical inquiry.