AI and ML enable sustainable practices, including in the education sector. As adaptive e-learning technologies emerges, there are opportunities in enhancing faculty development especially in multicultural learning environment as prevailing in Arab culture. This paper proposes an improvement to an existing e-learning model through the incorporation of ML with an adaptive e-learning model that is designed to improve the performance of the educators needed for the converging international twinning practices across culturally diverse faculty. Based on the findings of adaptive learning theories and collaborative learning approach, the study uses big data analytics to suggest relevant professional development tools and resources, active social media components as well as cross-national faculty matches for improved learner experience. The application of the proposed framework also encompasses the use of a recommendation system based on collaborative filtering and clustering to help create custom learning routes for individual faculty members. Moreover, learning analytics will be utilized based on the machine learning algorithms to compare learning progress and the level of participation of students, as well as cross-cultural engagement levels to evaluate the outcomes of multi faculty twinning and collaborative competencies development. Especially for the effective management of the social media-based educational information system, this model utilizes a dataset including faculty engagement statistics, feedback surveys, and interaction logs, to pinpoint competency deficiencies and promote learning communication.