The integration of Artificial Intelligence (AI) into language education has transformed the mechanisms through which teachers acquire knowledge, develop instructional capabilities, and improve pedagogical decision-making. However, effective AI adoption requires more than technological availability; it demands structured knowledge integration models that combine explicit instructional knowledge with tacit professional experiences. This research proposes an Intelligent AI-Assisted Model for Optimizing Teacher Competence Through Knowledge Integration in Language Education. The study develops a conceptual framework that integrates AI-supported learning analytics, knowledge-sharing mechanisms, emotional intelligence factors, and adaptive pedagogical optimization. The proposed model is theoretically grounded in second language acquisition research, teacher cognition, and technology-enhanced learning perspectives. Existing literature highlights the importance of emotional, cognitive, and environmental factors in language learning, while recent technological approaches demonstrate the potential of intelligent systems for improving knowledge accessibility and decision support. The framework emphasizes AI as an augmentation mechanism that enables teachers to transform accumulated professional knowledge into adaptive instructional strategies. The research further examines system components, implementation mechanisms, expected outcomes, and limitations associated with AI-assisted teacher development. Findings indicate that AI-driven knowledge integration can enhance teacher self-efficacy, instructional personalization, reflective practice, and continuous professional development. The study contributes a structured approach for applying AI technologies to strengthen teacher competence and advance intelligent language education ecosystems.