This paper explores the potential for integrating Artificial Intelligence (AI) into bilingual teaching and learning. In the modern educational system, technological innovations play a vital role in enhancing the quality of instruction. Bilingual education, which relies on the parallel use of two languages, requires individualized and flexible pedagogical approaches. The objective of this research is to develop an AI-based innovative model that ensures a personalized, adaptive, and data-driven learning process. The paper examines the primary educational capabilities of AI, including adaptive platforms, automated assessment, speech recognition, and progress analytics. It introduces a new model—the AI-Integrated Dynamic Bilingual Framework—which encompasses diagnostic assessment, linguistic balance control, and a feedback system. The study discusses the educational processes at the Georgian Technical University, specifically starting with the "1+4" program, where bilingualism is grounded in the primacy of the indigenous language, treated as either a foreign or second language. The paper emphasizes the prominent role of Linguo-cultural studies (Lingvocountryology) in bilingual instruction, the effectiveness of which is significantly bolstered by the involvement of AI. Given the novelty of the topic, the research tasks include: analyzing the theoretical foundations of bilingual education; establishing the principles of personalized learning; evaluating the educational potential of AI; defining innovative methods; and analyzing the advantages and challenges of utilizing AI in a bilingual environment. The bilingual framework, or the concept of bilingual education, is governed by the following: bilingual instruction is based on the equal or functional use of two languages in the learning process. Its goals are to develop linguistic competence, strengthen cognitive skills, and raise intercultural awareness. Research confirms that learning in a bilingual environment improves critical thinking and problem-solving skills. The methodology is based on qualitative analysis, case studies, experimental implementation (pilot class/group), questionnaires, and observation. Regarding innovative approaches, adaptive bilingual platforms analyze the learner's proficiency level